WorldWideScience

Sample records for relevant feature selection

  1. Relevant test set using feature selection algorithm for early detection ...

    African Journals Online (AJOL)

    The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification's accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we ...

  2. A DYNAMIC FEATURE SELECTION METHOD FOR DOCUMENT RANKING WITH RELEVANCE FEEDBACK APPROACH

    Directory of Open Access Journals (Sweden)

    K. Latha

    2010-07-01

    Full Text Available Ranking search results is essential for information retrieval and Web search. Search engines need to not only return highly relevant results, but also be fast to satisfy users. As a result, not all available features can be used for ranking, and in fact only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. In this paper we describe a 0/1 knapsack procedure for automatically selecting features to use within Generalization model for Document Ranking. We propose an approach for Relevance Feedback using Expectation Maximization method and evaluate the algorithm on the TREC Collection for describing classes of feedback textual information retrieval features. Experimental results, evaluated on standard TREC-9 part of the OHSUMED collections, show that our feature selection algorithm produces models that are either significantly more effective than, or equally effective as, models such as Markov Random Field model, Correlation Co-efficient and Count Difference method

  3. Feature-selective Attention in Frontoparietal Cortex: Multivoxel Codes Adjust to Prioritize Task-relevant Information.

    Science.gov (United States)

    Jackson, Jade; Rich, Anina N; Williams, Mark A; Woolgar, Alexandra

    2017-02-01

    Human cognition is characterized by astounding flexibility, enabling us to select appropriate information according to the objectives of our current task. A circuit of frontal and parietal brain regions, often referred to as the frontoparietal attention network or multiple-demand (MD) regions, are believed to play a fundamental role in this flexibility. There is evidence that these regions dynamically adjust their responses to selectively process information that is currently relevant for behavior, as proposed by the "adaptive coding hypothesis" [Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2, 820-829, 2001]. Could this provide a neural mechanism for feature-selective attention, the process by which we preferentially process one feature of a stimulus over another? We used multivariate pattern analysis of fMRI data during a perceptually challenging categorization task to investigate whether the representation of visual object features in the MD regions flexibly adjusts according to task relevance. Participants were trained to categorize visually similar novel objects along two orthogonal stimulus dimensions (length/orientation) and performed short alternating blocks in which only one of these dimensions was relevant. We found that multivoxel patterns of activation in the MD regions encoded the task-relevant distinctions more strongly than the task-irrelevant distinctions: The MD regions discriminated between stimuli of different lengths when length was relevant and between the same objects according to orientation when orientation was relevant. The data suggest a flexible neural system that adjusts its representation of visual objects to preferentially encode stimulus features that are currently relevant for behavior, providing a neural mechanism for feature-selective attention.

  4. Fuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection

    Directory of Open Access Journals (Sweden)

    Daren Yu

    2011-08-01

    Full Text Available Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information. In this paper we introduce the fuzzy information entropy and fuzzy mutual information for computing relevance between numerical or fuzzy features and decision. The relationship between fuzzy information entropy and differential entropy is also discussed. Moreover, we combine fuzzy mutual information with qmin-Redundancy-Max-Relevanceq, qMax-Dependencyq and min-Redundancy-Max-Dependencyq algorithms. The performance and stability of the proposed algorithms are tested on benchmark data sets. Experimental results show the proposed algorithms are effective and stable.

  5. Relevance feature selection of modal frequency-ambient condition pattern recognition in structural health assessment for reinforced concrete buildings

    Directory of Open Access Journals (Sweden)

    He-Qing Mu

    2016-08-01

    Full Text Available Modal frequency is an important indicator for structural health assessment. Previous studies have shown that this indicator is substantially affected by the fluctuation of ambient conditions, such as temperature and humidity. Therefore, recognizing the pattern between modal frequency and ambient conditions is necessary for reliable long-term structural health assessment. In this article, a novel machine-learning algorithm is proposed to automatically select relevance features in modal frequency-ambient condition pattern recognition based on structural dynamic response and ambient condition measurement. In contrast to the traditional feature selection approaches by examining a large number of combinations of extracted features, the proposed algorithm conducts continuous relevance feature selection by introducing a sophisticated hyperparameterization on the weight parameter vector controlling the relevancy of different features in the prediction model. The proposed algorithm is then utilized for structural health assessment for a reinforced concrete building based on 1-year daily measurements. It turns out that the optimal model class including the relevance features for each vibrational mode is capable to capture the pattern between the corresponding modal frequency and the ambient conditions.

  6. Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

    Science.gov (United States)

    Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

    2015-01-01

    We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

  7. Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

    Directory of Open Access Journals (Sweden)

    Xin Ma

    2015-01-01

    Full Text Available The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR method, followed by incremental feature selection (IFS. We incorporated features of conjoint triad features and three novel features: binding propensity (BP, nonbinding propensity (NBP, and evolutionary information combined with physicochemical properties (EIPP. The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient. High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

  8. Online feature selection with streaming features.

    Science.gov (United States)

    Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

    2013-05-01

    We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

  9. YamiPred: A novel evolutionary method for predicting pre-miRNAs and selecting relevant features

    KAUST Repository

    Kleftogiannis, Dimitrios A.; Theofilatos, Konstantinos; Likothanassis, Spiros; Mavroudi, Seferina

    2015-01-01

    MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of Support Vector Machines (SVM) with Genetic Algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

  10. YamiPred: A novel evolutionary method for predicting pre-miRNAs and selecting relevant features

    KAUST Repository

    Kleftogiannis, Dimitrios A.

    2015-01-23

    MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of Support Vector Machines (SVM) with Genetic Algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

  11. Feature Selection by Reordering

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2005-01-01

    Roč. 2, č. 1 (2005), s. 155-161 ISSN 1738-6438 Institutional research plan: CEZ:AV0Z10300504 Keywords : feature selection * data reduction * ordering of features Subject RIV: BA - General Mathematics

  12. Unsupervised Feature Subset Selection

    DEFF Research Database (Denmark)

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  13. Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models

    Science.gov (United States)

    Paja, W.; Wrzesień, M.; Niemiec, R.; Rudnicki, W. R.

    2015-07-01

    The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables.

  14. Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

    Science.gov (United States)

    Paja, Wiesław; Wrzesien, Mariusz; Niemiec, Rafał; Rudnicki, Witold R.

    2016-03-01

    Climate models are extremely complex pieces of software. They reflect the best knowledge on the physical components of the climate; nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a simulation crashing. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to the simulation crashing and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the data set used in this research using different methodology. We confirm the main conclusion of the original study concerning the suitability of machine learning for the prediction of crashes. We show that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three others are relevant but redundant and two are not relevant at all. We also show that the variance due to the split of data between training and validation sets has a large influence both on the accuracy of predictions and on the relative importance of variables; hence only a cross-validated approach can deliver a robust prediction of performance and relevance of variables.

  15. EOG feature relevance determination for microsleep detection

    Directory of Open Access Journals (Sweden)

    Golz Martin

    2017-09-01

    Full Text Available Automatic relevance determination (ARD was applied to two-channel EOG recordings for microsleep event (MSE recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC and logarithmic power spectral densities (PSD averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM, in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 – 4.9 % and 1.9 – 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 – 0.006 % and 0.002 – 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respectively. GRLVQ permits objective feature reduction by inclusion of all processing stages, but is not as accurate as SVM.

  16. EOG feature relevance determination for microsleep detection

    Directory of Open Access Journals (Sweden)

    Golz Martin

    2017-09-01

    Full Text Available Automatic relevance determination (ARD was applied to two-channel EOG recordings for microsleep event (MSE recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC and logarithmic power spectral densities (PSD averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM, in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respec-tively. GRLVQ permits objective feature reduction by inclu-sion of all processing stages, but is not as accurate as SVM.

  17. EOG feature relevance determination for microsleep detection

    OpenAIRE

    Golz Martin; Wollner Sebastian; Sommer David; Schnieder Sebastian

    2017-01-01

    Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD...

  18. Feature Selection with the Boruta Package

    OpenAIRE

    Kursa, Miron B.; Rudnicki, Witold R.

    2010-01-01

    This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.

  19. Feature Selection with the Boruta Package

    Directory of Open Access Journals (Sweden)

    Miron B. Kursa

    2010-10-01

    Full Text Available This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.

  20. Classification Using Markov Blanket for Feature Selection

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Luo, Jian

    2009-01-01

    Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm...... for filtering features and then applies a classifier using the Markov blanket predictors. The Markov blanket contains a minimal subset of relevant features that yields optimal classification performance. We experimentally demonstrate the improved performance of several classifiers using a Markov blanket...... induction as a feature selection method. In addition, we point out an important assumption behind the Markov blanket induction algorithm and show its effect on the classification performance....

  1. Predictive Feature Selection for Genetic Policy Search

    Science.gov (United States)

    2014-05-22

    limited manual intervention are becoming increasingly desirable as more complex tasks in dynamic and high- tempo environments are explored. Reinforcement...states in many domains causes features relevant to the reward variations to be overlooked, which hinders the policy search. 3.4 Parameter Selection PFS...the current feature subset. This local minimum may be “deceptive,” meaning that it does not clearly lead to the global optimal policy ( Goldberg and

  2. Inferring feature relevances from metric learning

    DEFF Research Database (Denmark)

    Schulz, Alexander; Mokbel, Bassam; Biehl, Michael

    2015-01-01

    Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval, but which also enable the interpretability of these operations by assigning explicit relevance weights...

  3. Feature selection toolbox software package

    Czech Academy of Sciences Publication Activity Database

    Pudil, Pavel; Novovičová, Jana; Somol, Petr

    2002-01-01

    Roč. 23, č. 4 (2002), s. 487-492 ISSN 0167-8655 R&D Projects: GA ČR GA402/01/0981 Institutional research plan: CEZ:AV0Z1075907 Keywords : pattern recognition * feature selection * loating search algorithms Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.409, year: 2002

  4. Attentional Selection of Feature Conjunctions Is Accomplished by Parallel and Independent Selection of Single Features.

    Science.gov (United States)

    Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A

    2015-07-08

    Experiments that study feature-based attention have often examined situations in which selection is based on a single feature (e.g., the color red). However, in more complex situations relevant stimuli may not be set apart from other stimuli by a single defining property but by a specific combination of features. Here, we examined sustained attentional selection of stimuli defined by conjunctions of color and orientation. Human observers attended to one out of four concurrently presented superimposed fields of randomly moving horizontal or vertical bars of red or blue color to detect brief intervals of coherent motion. Selective stimulus processing in early visual cortex was assessed by recordings of steady-state visual evoked potentials (SSVEPs) elicited by each of the flickering fields of stimuli. We directly contrasted attentional selection of single features and feature conjunctions and found that SSVEP amplitudes on conditions in which selection was based on a single feature only (color or orientation) exactly predicted the magnitude of attentional enhancement of SSVEPs when attending to a conjunction of both features. Furthermore, enhanced SSVEP amplitudes elicited by attended stimuli were accompanied by equivalent reductions of SSVEP amplitudes elicited by unattended stimuli in all cases. We conclude that attentional selection of a feature-conjunction stimulus is accomplished by the parallel and independent facilitation of its constituent feature dimensions in early visual cortex. The ability to perceive the world is limited by the brain's processing capacity. Attention affords adaptive behavior by selectively prioritizing processing of relevant stimuli based on their features (location, color, orientation, etc.). We found that attentional mechanisms for selection of different features belonging to the same object operate independently and in parallel: concurrent attentional selection of two stimulus features is simply the sum of attending to each of those

  5. Feature Selection via Chaotic Antlion Optimization.

    Directory of Open Access Journals (Sweden)

    Hossam M Zawbaa

    Full Text Available Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting while minimizing the number of features used.We propose an optimization approach for the feature selection problem that considers a "chaotic" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

  6. Oculomotor selection underlies feature retention in visual working memory.

    Science.gov (United States)

    Hanning, Nina M; Jonikaitis, Donatas; Deubel, Heiner; Szinte, Martin

    2016-02-01

    Oculomotor selection, spatial task relevance, and visual working memory (WM) are described as three processes highly intertwined and sustained by similar cortical structures. However, because task-relevant locations always constitute potential saccade targets, no study so far has been able to distinguish between oculomotor selection and spatial task relevance. We designed an experiment that allowed us to dissociate in humans the contribution of task relevance, oculomotor selection, and oculomotor execution to the retention of feature representations in WM. We report that task relevance and oculomotor selection lead to dissociable effects on feature WM maintenance. In a first task, in which an object's location was encoded as a saccade target, its feature representations were successfully maintained in WM, whereas they declined at nonsaccade target locations. Likewise, we observed a similar WM benefit at the target of saccades that were prepared but never executed. In a second task, when an object's location was marked as task relevant but constituted a nonsaccade target (a location to avoid), feature representations maintained at that location did not benefit. Combined, our results demonstrate that oculomotor selection is consistently associated with WM, whereas task relevance is not. This provides evidence for an overlapping circuitry serving saccade target selection and feature-based WM that can be dissociated from processes encoding task-relevant locations. Copyright © 2016 the American Physiological Society.

  7. Feature Selection Using Adaboost for Face Expression Recognition

    National Research Council Canada - National Science Library

    Silapachote, Piyanuch; Karuppiah, Deepak R; Hanson, Allen R

    2005-01-01

    We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information...

  8. Selection of relevant dietary indicators for health.

    Science.gov (United States)

    Steingrímsdóttir, L; Ovesen, L; Moreiras, O; Jacob, S

    2002-05-01

    To define a set of dietary components that are relevant determinants for health in Europe. The selected components are intended to serve as nutrition indicators for health in the European Health Monitoring Programme and, as such, must be limited in number, relevant to health in Europe and practical for all involved countries with respect to data gathering and comparability of data. Major nutrition factors were determined by reviewing relevant epidemiological and clinical literature in nutrition and health as well as referring to reports from international expert groups, including the report from the project Nutrition and Diet for Healthy Lifestyles in Europe. The selection of factors was also based on the relative ease and cost involved for participating countries to obtain comparable and valid data. The selected factors include foods or food groups as well as individual nutrients. Biomarkers are suggested for selected nutrients that pose the greatest difficulty in obtaining valid and comparable data from dietary studies. The following list of diet indicators for health monitoring in Europe was agreed upon by the EFCOSUM group in 2001, in order of priority: vegetables, fruit, bread, fish, saturated fatty acids as percentage of energy (%E), total fat as %E, and ethanol in grams per day. Biomarkers were suggested for the following nutrients: folate, vitamin D, iron, iodine and sodium. Energy has to be assessed in order to calculate %E from total fat and saturated fatty acids.

  9. Discriminative semi-supervised feature selection via manifold regularization.

    Science.gov (United States)

    Xu, Zenglin; King, Irwin; Lyu, Michael Rung-Tsong; Jin, Rong

    2010-07-01

    Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed approach selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. In comparison with previous semi-supervised feature selection algorithms, our proposed semi-supervised feature selection method is an embedded feature selection method and is able to find more discriminative features. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

  10. Numerical Analysis for Relevant Features in Intrusion Detection (NARFid)

    Science.gov (United States)

    2009-03-01

    Error and Average Correlation Coefficient. Mucciardi and Gose [63] discuss seven methods for selecting features. These methods seek to overcome the...POEmaxPOEmin). (2.37) With each iteration of selecting the next feature, ACC is also normalized in the same fashion. As stated by Mucciardi and Gose ...lan’s discussion [70] as described in Section 2.3.1. Mucciardi and Gose [63] provide the POEACC parameters that perform well in their experiments. As

  11. Identifying relevant feature-action associations for grasping unmodelled objects

    DEFF Research Database (Denmark)

    Thomsen, Mikkel Tang; Kraft, Dirk; Krüger, Norbert

    2015-01-01

    content. The method is provided with a large and structured set of visual features, motivated by the visual hierarchy in primates and finds relevant feature action associations automatically. We apply our method in a simulated environment on three different object sets for the case of grasp affordance...... learning. For box objects, we achieve a 0.90 success probability, 0.80 for round objects and up to 0.75 for open objects, when presented with novel objects. In this work, we in particular demonstrate the effect of choosing appropriate feature representations. We demonstrate a significant performance...

  12. A DSS framework for maintaining relevant features of the Small Business B2C Websites

    OpenAIRE

    Khatun, Madhury; Miah, Shah Jahan

    2016-01-01

    Managers are heavily engaged in strategic decision-making for businesses, particularly in a changing environment. One of the most important decisions for online small businesses, as part of their strategic planning, is selecting relevant features on their websites, both to attract and interact with consumers. However, only a few Australian small businesses use strategic tools for selecting their website features. As a result, businesses lose potential domestic sales in the business-to-consume...

  13. Feature selection for portfolio optimization

    DEFF Research Database (Denmark)

    Bjerring, Thomas Trier; Ross, Omri; Weissensteiner, Alex

    2016-01-01

    Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major....... While most of the diversification benefits are preserved, the parameter estimation problem is alleviated. We conduct out-of-sample back-tests to show that in most cases different well-established portfolio selection rules applied on the reduced asset universe are able to improve alpha relative...

  14. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

  15. SIP-FS: a novel feature selection for data representation

    Directory of Open Access Journals (Sweden)

    Yiyou Guo

    2018-02-01

    Full Text Available Abstract Multiple features are widely used to characterize real-world datasets. It is desirable to select leading features with stability and interpretability from a set of distinct features for a comprehensive data description. However, most of existing feature selection methods focus on the predictability (e.g., prediction accuracy of selected results yet neglect stability. To obtain compact data representation, a novel feature selection method is proposed to improve stability, and interpretability without sacrificing predictability (SIP-FS. Instead of mutual information, generalized correlation is adopted in minimal redundancy maximal relevance to measure the relation between different feature types. Several feature types (each contains a certain number of features can then be selected and evaluated quantitatively to determine what types contribute to a specific class, thereby enhancing the so-called interpretability of features. Moreover, stability is introduced in the criterion of SIP-FS to obtain consistent results of ranking. We conduct experiments on three publicly available datasets using one-versus-all strategy to select class-specific features. The experiments illustrate that SIP-FS achieves significant performance improvements in terms of stability and interpretability with desirable prediction accuracy and indicates advantages over several state-of-the-art approaches.

  16. Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.

    Science.gov (United States)

    Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

    2016-01-13

    An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.

  17. Attention improves encoding of task-relevant features in the human visual cortex

    Science.gov (United States)

    Jehee, Janneke F.M.; Brady, Devin K.; Tong, Frank

    2011-01-01

    When spatial attention is directed towards a particular stimulus, increased activity is commonly observed in corresponding locations of the visual cortex. Does this attentional increase in activity indicate improved processing of all features contained within the attended stimulus, or might spatial attention selectively enhance the features relevant to the observer’s task? We used fMRI decoding methods to measure the strength of orientation-selective activity patterns in the human visual cortex while subjects performed either an orientation or contrast discrimination task, involving one of two laterally presented gratings. Greater overall BOLD activation with spatial attention was observed in areas V1-V4 for both tasks. However, multivariate pattern analysis revealed that orientation-selective responses were enhanced by attention only when orientation was the task-relevant feature, and not when the grating’s contrast had to be attended. In a second experiment, observers discriminated the orientation or color of a specific lateral grating. Here, orientation-selective responses were enhanced in both tasks but color-selective responses were enhanced only when color was task-relevant. In both experiments, task-specific enhancement of feature-selective activity was not confined to the attended stimulus location, but instead spread to other locations in the visual field, suggesting the concurrent involvement of a global feature-based attentional mechanism. These results suggest that attention can be remarkably selective in its ability to enhance particular task-relevant features, and further reveal that increases in overall BOLD amplitude are not necessarily accompanied by improved processing of stimulus information. PMID:21632942

  18. Feature selection for splice site prediction: A new method using EDA-based feature ranking

    Directory of Open Access Journals (Sweden)

    Rouzé Pierre

    2004-05-01

    Full Text Available Abstract Background The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. Conclusion We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.

  19. On the relevance of spectral features for instrument classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2007-01-01

    Automatic knowledge extraction from music signals is a key component for most music organization and music information retrieval systems. In this paper, we consider the problem of instrument modelling and instrument classification from the rough audio data. Existing systems for automatic instrument...... classification operate normally on a relatively large number of features, from which those related to the spectrum of the audio signal are particularly relevant. In this paper, we confront two different models about the spectral characterization of musical instruments. The first assumes a constant envelope...

  20. Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis

    Directory of Open Access Journals (Sweden)

    Zare Habil

    2013-01-01

    Full Text Available Abstract One challenge in applying bioinformatic tools to clinical or biological data is high number of features that might be provided to the learning algorithm without any prior knowledge on which ones should be used. In such applications, the number of features can drastically exceed the number of training instances which is often limited by the number of available samples for the study. The Lasso is one of many regularization methods that have been developed to prevent overfitting and improve prediction performance in high-dimensional settings. In this paper, we propose a novel algorithm for feature selection based on the Lasso and our hypothesis is that defining a scoring scheme that measures the "quality" of each feature can provide a more robust feature selection method. Our approach is to generate several samples from the training data by bootstrapping, determine the best relevance-ordering of the features for each sample, and finally combine these relevance-orderings to select highly relevant features. In addition to the theoretical analysis of our feature scoring scheme, we provided empirical evaluations on six real datasets from different fields to confirm the superiority of our method in exploratory data analysis and prediction performance. For example, we applied FeaLect, our feature scoring algorithm, to a lymphoma dataset, and according to a human expert, our method led to selecting more meaningful features than those commonly used in the clinics. This case study built a basis for discovering interesting new criteria for lymphoma diagnosis. Furthermore, to facilitate the use of our algorithm in other applications, the source code that implements our algorithm was released as FeaLect, a documented R package in CRAN.

  1. Penalized feature selection and classification in bioinformatics

    OpenAIRE

    Ma, Shuangge; Huang, Jian

    2008-01-01

    In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classific...

  2. Tracing the breeding farm of domesticated pig using feature selection (

    Directory of Open Access Journals (Sweden)

    Taehyung Kwon

    2017-11-01

    Full Text Available Objective Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP markers strictly selected through least absolute shrinkage and selection operator (LASSO feature selection. Methods We performed farm tracing of domesticated pig (Sus scrofa from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

  3. Embedded Incremental Feature Selection for Reinforcement Learning

    Science.gov (United States)

    2012-05-01

    Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

  4. Doubly sparse factor models for unifying feature transformation and feature selection

    International Nuclear Information System (INIS)

    Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato; Matsumoto, Narihisa; Sugase-Miyamoto, Yasuko

    2010-01-01

    A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

  5. Doubly sparse factor models for unifying feature transformation and feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato [ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Saitama (Japan); Matsumoto, Narihisa; Sugase-Miyamoto, Yasuko, E-mail: okada@k.u-tokyo.ac.j [Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki (Japan)

    2010-06-01

    A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

  6. A redundancy-removing feature selection algorithm for nominal data

    Directory of Open Access Journals (Sweden)

    Zhihua Li

    2015-10-01

    Full Text Available No order correlation or similarity metric exists in nominal data, and there will always be more redundancy in a nominal dataset, which means that an efficient mutual information-based nominal-data feature selection method is relatively difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.

  7. Aging, selective attention, and feature integration.

    Science.gov (United States)

    Plude, D J; Doussard-Roosevelt, J A

    1989-03-01

    This study used feature-integration theory as a means of determining the point in processing at which selective attention deficits originate. The theory posits an initial stage of processing in which features are registered in parallel and then a serial process in which features are conjoined to form complex stimuli. Performance of young and older adults on feature versus conjunction search is compared. Analyses of reaction times and error rates suggest that elderly adults in addition to young adults, can capitalize on the early parallel processing stage of visual information processing, and that age decrements in visual search arise as a result of the later, serial stage of processing. Analyses of a third, unconfounded, conjunction search condition reveal qualitatively similar modes of conjunction search in young and older adults. The contribution of age-related data limitations is found to be secondary to the contribution of age decrements in selective attention.

  8. Feature relevance assessment for the semantic interpretation of 3D point cloud data

    Directory of Open Access Journals (Sweden)

    M. Weinmann

    2013-10-01

    Full Text Available The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

  9. Feature Selection Based on Mutual Correlation

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Somol, Petr; Ververidis, D.; Kotropoulos, C.

    2006-01-01

    Roč. 19, č. 4225 (2006), s. 569-577 ISSN 0302-9743. [Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./. Cancun, 14.11.2006-17.11.2006] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection Subject RIV: BD - Theory of Information Impact factor: 0.402, year: 2005 http://library.utia.cas.cz/separaty/historie/haindl-feature selection based on mutual correlation.pdf

  10. Feature and Region Selection for Visual Learning.

    Science.gov (United States)

    Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

    2016-03-01

    Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

  11. DYNAMIC FEATURE SELECTION FOR WEB USER IDENTIFICATION ON LINGUISTIC AND STYLISTIC FEATURES OF ONLINE TEXTS

    Directory of Open Access Journals (Sweden)

    A. A. Vorobeva

    2017-01-01

    Full Text Available The paper deals with identification and authentication of web users participating in the Internet information processes (based on features of online texts.In digital forensics web user identification based on various linguistic features can be used to discover identity of individuals, criminals or terrorists using the Internet to commit cybercrimes. Internet could be used as a tool in different types of cybercrimes (fraud and identity theft, harassment and anonymous threats, terrorist or extremist statements, distribution of illegal content and information warfare. Linguistic identification of web users is a kind of biometric identification, it can be used to narrow down the suspects, identify a criminal and prosecute him. Feature set includes various linguistic and stylistic features extracted from online texts. We propose dynamic feature selection for each web user identification task. Selection is based on calculating Manhattan distance to k-nearest neighbors (Relief-f algorithm. This approach improves the identification accuracy and minimizes the number of features. Experiments were carried out on several datasets with different level of class imbalance. Experiment results showed that features relevance varies in different set of web users (probable authors of some text; features selection for each set of web users improves identification accuracy by 4% at the average that is approximately 1% higher than with the use of static set of features. The proposed approach is most effective for a small number of training samples (messages per user.

  12. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

    Science.gov (United States)

    Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Fu, Jing-Jing; Xu, Zhao-Xia; Guo, Rui; Qian, Peng

    2012-01-01

    Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:22719781

  13. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

    Directory of Open Access Journals (Sweden)

    Guo-Ping Liu

    2012-01-01

    Full Text Available Background. In Traditional Chinese Medicine (TCM, most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs. Methods. We employed a multilabel learning using the relevant feature for each label (REAL algorithm to construct a syndrome diagnostic model for chronic gastritis (CG in TCM. REAL combines feature selection methods to select the significant symptoms (signs of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL, whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

  14. Hierarchical feature selection for erythema severity estimation

    Science.gov (United States)

    Wang, Li; Shi, Chenbo; Shu, Chang

    2014-10-01

    At present PASI system of scoring is used for evaluating erythema severity, which can help doctors to diagnose psoriasis [1-3]. The system relies on the subjective judge of doctors, where the accuracy and stability cannot be guaranteed [4]. This paper proposes a stable and precise algorithm for erythema severity estimation. Our contributions are twofold. On one hand, in order to extract the multi-scale redness of erythema, we design the hierarchical feature. Different from traditional methods, we not only utilize the color statistical features, but also divide the detect window into small window and extract hierarchical features. Further, a feature re-ranking step is introduced, which can guarantee that extracted features are irrelevant to each other. On the other hand, an adaptive boosting classifier is applied for further feature selection. During the step of training, the classifier will seek out the most valuable feature for evaluating erythema severity, due to its strong learning ability. Experimental results demonstrate the high precision and robustness of our algorithm. The accuracy is 80.1% on the dataset which comprise 116 patients' images with various kinds of erythema. Now our system has been applied for erythema medical efficacy evaluation in Union Hosp, China.

  15. Adversarial Feature Selection Against Evasion Attacks.

    Science.gov (United States)

    Zhang, Fei; Chan, Patrick P K; Biggio, Battista; Yeung, Daniel S; Roli, Fabio

    2016-03-01

    Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection. In this paper, we provide a more detailed investigation of this aspect, shedding some light on the security properties of feature selection against evasion attacks. Inspired by previous work on adversary-aware classifiers, we propose a novel adversary-aware feature selection model that can improve classifier security against evasion attacks, by incorporating specific assumptions on the adversary's data manipulation strategy. We focus on an efficient, wrapper-based implementation of our approach, and experimentally validate its soundness on different application examples, including spam and malware detection.

  16. About the composition of self-relevance: Conjunctions not features are bound to the self.

    Science.gov (United States)

    Schäfer, Sarah; Frings, Christian; Wentura, Dirk

    2016-06-01

    Sui and colleagues (Journal of Experimental Psychology: Human Perception and Performance, 38, 1105-1117, 2012) introduced a matching paradigm to investigate prioritized processing of instructed self-relevance. They arbitrarily assigned simple geometric shapes to the participant and two other persons. Subsequently, the task was to judge whether label-shape pairings matched or not. The authors found a remarkable self-prioritization effect, that is, for matching self-related trials verification was very fast and accurate in comparison to the non-matching conditions. We analyzed whether single features or feature conjunctions are tagged to the self. In particular, we assigned colored shapes to the labels and included partial-matching trials (i.e., trials in which only one feature matched the label, whereas the other feature did not match the label). If single features are tagged to the self, partial matches would result in interference, whereas they should elicit the same data pattern as non-matching trials if only feature conjunctions are tagged to the self. Our data suggest the latter; only feature conjunctions are tagged to the self and are processed in a prioritized manner. This result emphasizes the functionality of self-relevance as a selection mechanism.

  17. A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization.

    Science.gov (United States)

    He, Xiaofei; Ji, Ming; Zhang, Chiyuan; Bao, Hujun

    2011-10-01

    In many information processing tasks, one is often confronted with very high-dimensional data. Feature selection techniques are designed to find the meaningful feature subset of the original features which can facilitate clustering, classification, and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenarios, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. Based on Laplacian regularized least squares, which finds a smooth function on the data manifold and minimizes the empirical loss, we propose two novel feature selection algorithms which aim to minimize the expected prediction error of the regularized regression model. Specifically, we select those features such that the size of the parameter covariance matrix of the regularized regression model is minimized. Motivated from experimental design, we use trace and determinant operators to measure the size of the covariance matrix. Efficient computational schemes are also introduced to solve the corresponding optimization problems. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithms.

  18. Finger vein recognition with personalized feature selection.

    Science.gov (United States)

    Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Meng, Xianjing

    2013-08-22

    Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

  19. Finger Vein Recognition with Personalized Feature Selection

    Directory of Open Access Journals (Sweden)

    Xianjing Meng

    2013-08-01

    Full Text Available Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG. For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

  20. Feature extraction and sensor selection for NPP initiating event identification

    International Nuclear Information System (INIS)

    Lin, Ting-Han; Wu, Shun-Chi; Chen, Kuang-You; Chou, Hwai-Pwu

    2017-01-01

    Highlights: • A two-stage feature extraction scheme for NPP initiating event identification. • With stBP, interrelations among the sensors can be retained for identification. • With dSFS, sensors that are crucial for identification can be efficiently selected. • Efficacy of the scheme is illustrated with data from the Maanshan NPP simulator. - Abstract: Initiating event identification is essential in managing nuclear power plant (NPP) severe accidents. In this paper, a novel two-stage feature extraction scheme that incorporates the proposed sensor type-wise block projection (stBP) and deflatable sequential forward selection (dSFS) is used to elicit the discriminant information in the data obtained from various NPP sensors to facilitate event identification. With the stBP, the primal features can be extracted without eliminating the interrelations among the sensors of the same type. The extracted features are then subjected to a further dimensionality reduction by selecting the sensors that are most relevant to the events under consideration. This selection is not easy, and a combinatorial optimization technique is normally required. With the dSFS, an optimal sensor set can be found with less computational load. Moreover, its sensor deflation stage allows sensors in the preselected set to be iteratively refined to avoid being trapped into a local optimum. Results from detailed experiments containing data of 12 event categories and a total of 112 events generated with a Taiwan’s Maanshan NPP simulator are presented to illustrate the efficacy of the proposed scheme.

  1. Receptive fields selection for binary feature description.

    Science.gov (United States)

    Fan, Bin; Kong, Qingqun; Trzcinski, Tomasz; Wang, Zhiheng; Pan, Chunhong; Fua, Pascal

    2014-06-01

    Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven method for designing binary feature descriptor, which we call receptive fields descriptor (RFD). Technically, RFD is constructed by thresholding responses of a set of receptive fields, which are selected from a large number of candidates according to their distinctiveness and correlations in a greedy way. Using two different kinds of receptive fields (namely rectangular pooling area and Gaussian pooling area) for selection, we obtain two binary descriptors RFDR and RFDG .accordingly. Image matching experiments on the well-known patch data set and Oxford data set demonstrate that RFD significantly outperforms the state-of-the-art binary descriptors, and is comparable with the best float-valued descriptors at a fraction of processing time. Finally, experiments on object recognition tasks confirm that both RFDR and RFDG successfully bridge the performance gap between binary descriptors and their floating-point competitors.

  2. Relevance of echo-structure and texture features

    DEFF Research Database (Denmark)

    Karemore, Gopal; Mullick, Jhinuk Basu; KV, Dr. Rajagopal

    2010-01-01

    Aim: Echostructure is an essential parameter for the evaluation of circumscribed lesions and can be described as a texture feature on ultrasound images. Present study evaluates the possibility of distinguishing between benign and malignant breast tumors using various texture features. Materials...... and Methods: 58 cases of breast tumor (29 each from benign and malignant) were documented under standardized conditions using a linear array machine and 7.5 MHz transducer. In each sonographic image, ROI of tumor was marked and then subjected to the evaluation of tumor status using five parameters of second...... performance ROC= 0.78(pbenign and malignant tumors. It also reveals that when evaluating images of a breast tumor...

  3. NetProt: Complex-based Feature Selection.

    Science.gov (United States)

    Goh, Wilson Wen Bin; Wong, Limsoon

    2017-08-04

    Protein complex-based feature selection (PCBFS) provides unparalleled reproducibility with high phenotypic relevance on proteomics data. Currently, there are five PCBFS paradigms, but not all representative methods have been implemented or made readily available. To allow general users to take advantage of these methods, we developed the R-package NetProt, which provides implementations of representative feature-selection methods. NetProt also provides methods for generating simulated differential data and generating pseudocomplexes for complex-based performance benchmarking. The NetProt open source R package is available for download from https://github.com/gohwils/NetProt/releases/ , and online documentation is available at http://rpubs.com/gohwils/204259 .

  4. Feature Selection for Chemical Sensor Arrays Using Mutual Information

    Science.gov (United States)

    Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

    2014-01-01

    We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

  5. Distinguishing the relevant features of frequent suicide attempters.

    Science.gov (United States)

    Lopez-Castroman, Jorge; Perez-Rodriguez, Maria de las Mercedes; Jaussent, Isabelle; Alegria, Analucia A; Artes-Rodriguez, Antonio; Freed, Peter; Guillaume, Sébastien; Jollant, Fabrice; Leiva-Murillo, Jose Miguel; Malafosse, Alain; Oquendo, Maria A; de Prado-Cumplido, Mario; Saiz-Ruiz, Jeronimo; Baca-Garcia, Enrique; Courtet, Philippe

    2011-05-01

    In spite of the high prevalence of suicide behaviours and the magnitude of the resultant burden, little is known about why individuals reattempt. We aim to investigate the relationships between clinical risk factors and the repetition of suicidal attempts. 1349 suicide attempters were consecutively recruited in the Emergency Room (ER) of two academic hospitals in France and Spain. Patients were extensively assessed and demographic and clinical data obtained. Data mining was used to determine the minimal number of variables that blinded the rest in relation to the number of suicide attempts. Using this set, a probabilistic graph ranking relationships with the target variable was constructed. The most common diagnoses among suicide attempters were affective disorders, followed by anxiety disorders. Risk of frequent suicide attempt was highest among middle-aged subjects, and diminished progressively with advancing age of onset at first attempt. Anxiety disorders significantly increased the risk of presenting frequent suicide attempts. Pathway analysis also indicated that frequent suicide attempts were linked to greater odds for alcohol and substance abuse disorders and more intensive treatment. Novel statistical methods found several clinical features that were associated with a history of frequent suicide attempts. The identified pathways may promote new hypothesis-driven studies of suicide attempts and preventive strategies. Copyright © 2010 Elsevier Ltd. All rights reserved.

  6. Feature Selection for Wheat Yield Prediction

    Science.gov (United States)

    Ruß, Georg; Kruse, Rudolf

    Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an everincreasing population by taking advantage of a field’s heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields’ heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.

  7. Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

    Science.gov (United States)

    Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

    2015-04-01

    Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

    Directory of Open Access Journals (Sweden)

    Ratri Enggar Pawening

    2016-06-01

    Full Text Available Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI for classifying heterogeneous features. We use unsupervised feature transformation (UFT methods and joint mutual information maximation (JMIM methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

  9. Naive Bayes-Guided Bat Algorithm for Feature Selection

    Directory of Open Access Journals (Sweden)

    Ahmed Majid Taha

    2013-01-01

    Full Text Available When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.

  10. Naive Bayes-Guided Bat Algorithm for Feature Selection

    Science.gov (United States)

    Taha, Ahmed Majid; Mustapha, Aida; Chen, Soong-Der

    2013-01-01

    When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. PMID:24396295

  11. Efficient Generation and Selection of Combined Features for Improved Classification

    KAUST Repository

    Shono, Ahmad N.

    2014-05-01

    This study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original feature set, for efficiently selecting the most discriminating of these generated combined features, and for efficiently performing a preliminary comparison of the classification results when using the original features exclusively against the results when using the selected combined features. The potential benefit of considering combined features in classification problems is demonstrated by applying the developed methodology and toolkit to three sample data sets where the discovery of combined features containing new discriminating information led to improved classification results.

  12. Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

    Directory of Open Access Journals (Sweden)

    Lijun Wang

    2013-01-01

    Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

  13. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    KAUST Repository

    Soufan, Othman

    2012-01-01

    proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a

  14. EEG feature selection method based on decision tree.

    Science.gov (United States)

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  15. The Effects of Goal Relevance and Perceptual Features on Emotional Items and Associative Memory

    Directory of Open Access Journals (Sweden)

    Wei B. Mao

    2017-07-01

    Full Text Available Showing an emotional item in a neutral background scene often leads to enhanced memory for the emotional item and impaired associative memory for background details. Meanwhile, both top–down goal relevance and bottom–up perceptual features played important roles in memory binding. We conducted two experiments and aimed to further examine the effects of goal relevance and perceptual features on emotional items and associative memory. By manipulating goal relevance (asking participants to categorize only each item image as living or non-living or to categorize each whole composite picture consisted of item image and background scene as natural scene or manufactured scene and perceptual features (controlling visual contrast and visual familiarity in two experiments, we found that both high goal relevance and salient perceptual features (high salience of items vs. high familiarity of items could promote emotional item memory, but they had different effects on associative memory for emotional items and neutral backgrounds. Specifically, high goal relevance and high perceptual-salience of items could jointly impair the associative memory for emotional items and neutral backgrounds, while the effect of item familiarity on associative memory for emotional items would be modulated by goal relevance. High familiarity of items could increase associative memory for negative items and neutral backgrounds only in the low goal relevance condition. These findings suggest the effect of emotion on associative memory is not only related to attentional capture elicited by emotion, but also can be affected by goal relevance and perceptual features of stimulus.

  16. The Effects of Goal Relevance and Perceptual Features on Emotional Items and Associative Memory.

    Science.gov (United States)

    Mao, Wei B; An, Shu; Yang, Xiao F

    2017-01-01

    Showing an emotional item in a neutral background scene often leads to enhanced memory for the emotional item and impaired associative memory for background details. Meanwhile, both top-down goal relevance and bottom-up perceptual features played important roles in memory binding. We conducted two experiments and aimed to further examine the effects of goal relevance and perceptual features on emotional items and associative memory. By manipulating goal relevance (asking participants to categorize only each item image as living or non-living or to categorize each whole composite picture consisted of item image and background scene as natural scene or manufactured scene) and perceptual features (controlling visual contrast and visual familiarity) in two experiments, we found that both high goal relevance and salient perceptual features (high salience of items vs. high familiarity of items) could promote emotional item memory, but they had different effects on associative memory for emotional items and neutral backgrounds. Specifically, high goal relevance and high perceptual-salience of items could jointly impair the associative memory for emotional items and neutral backgrounds, while the effect of item familiarity on associative memory for emotional items would be modulated by goal relevance. High familiarity of items could increase associative memory for negative items and neutral backgrounds only in the low goal relevance condition. These findings suggest the effect of emotion on associative memory is not only related to attentional capture elicited by emotion, but also can be affected by goal relevance and perceptual features of stimulus.

  17. Input significance analysis: feature selection through synaptic ...

    African Journals Online (AJOL)

    Connection Weights (CW) and Garson's Algorithm (GA) and the classifier selected ... from the UCI Machine Learning Repository and executed in an online ... connectionist systems; evolving fuzzy neural network; connection weights; Garson's

  18. Classification Influence of Features on Given Emotions and Its Application in Feature Selection

    Science.gov (United States)

    Xing, Yin; Chen, Chuang; Liu, Li-Long

    2018-04-01

    In order to solve the problem that there is a large amount of redundant data in high-dimensional speech emotion features, we analyze deeply the extracted speech emotion features and select better features. Firstly, a given emotion is classified by each feature. Secondly, the recognition rate is ranked in descending order. Then, the optimal threshold of features is determined by rate criterion. Finally, the better features are obtained. When applied in Berlin and Chinese emotional data set, the experimental results show that the feature selection method outperforms the other traditional methods.

  19. Crossmodal integration enhances neural representation of task-relevant features in audiovisual face perception.

    Science.gov (United States)

    Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Liu, Yongjian; Liang, Changhong; Sun, Pei

    2015-02-01

    Previous studies have shown that audiovisual integration improves identification performance and enhances neural activity in heteromodal brain areas, for example, the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG). Furthermore, it has also been demonstrated that attention plays an important role in crossmodal integration. In this study, we considered crossmodal integration in audiovisual facial perception and explored its effect on the neural representation of features. The audiovisual stimuli in the experiment consisted of facial movie clips that could be classified into 2 gender categories (male vs. female) or 2 emotion categories (crying vs. laughing). The visual/auditory-only stimuli were created from these movie clips by removing the auditory/visual contents. The subjects needed to make a judgment about the gender/emotion category for each movie clip in the audiovisual, visual-only, or auditory-only stimulus condition as functional magnetic resonance imaging (fMRI) signals were recorded. The neural representation of the gender/emotion feature was assessed using the decoding accuracy and the brain pattern-related reproducibility indices, obtained by a multivariate pattern analysis method from the fMRI data. In comparison to the visual-only and auditory-only stimulus conditions, we found that audiovisual integration enhanced the neural representation of task-relevant features and that feature-selective attention might play a role of modulation in the audiovisual integration. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  20. System for selecting relevant information for decision support.

    Science.gov (United States)

    Kalina, Jan; Seidl, Libor; Zvára, Karel; Grünfeldová, Hana; Slovák, Dalibor; Zvárová, Jana

    2013-01-01

    We implemented a prototype of a decision support system called SIR which has a form of a web-based classification service for diagnostic decision support. The system has the ability to select the most relevant variables and to learn a classification rule, which is guaranteed to be suitable also for high-dimensional measurements. The classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. The implemented prototype was tested on a sample of patients in a cardiological study and performs an information extraction from a high-dimensional set containing both clinical and gene expression data.

  1. Selected PET radiomic features remain the same.

    Science.gov (United States)

    Tsujikawa, Tetsuya; Tsuyoshi, Hideaki; Kanno, Masafumi; Yamada, Shizuka; Kobayashi, Masato; Narita, Norihiko; Kimura, Hirohiko; Fujieda, Shigeharu; Yoshida, Yoshio; Okazawa, Hidehiko

    2018-04-17

    We investigated whether PET radiomic features are affected by differences in the scanner, scan protocol, and lesion location using 18 F-FDG PET/CT and PET/MR scans. SUV, TMR, skewness, kurtosis, entropy, and homogeneity strongly correlated between PET/CT and PET/MR images. SUVs were significantly higher on PET/MR 0-2 min and PET/MR 0-10 min than on PET/CT in gynecological cancer ( p = 0.008 and 0.008, respectively), whereas no significant difference was observed between PET/CT, PET/MR 0-2 min , and PET/MR 0-10 min images in oral cavity/oropharyngeal cancer. TMRs on PET/CT, PET/MR 0-2 min , and PET/MR 0-10 min increased in this order in gynecological cancer and oral cavity/oropharyngeal cancer. In contrast to conventional and histogram indices, 4 textural features (entropy, homogeneity, SRE, and LRE) were not significantly different between PET/CT, PET/MR 0-2 min , and PET/MR 0-10 min images. 18 F-FDG PET radiomic features strongly correlated between PET/CT and PET/MR images. Dixon-based attenuation correction on PET/MR images underestimated tumor tracer uptake more significantly in oral cavity/oropharyngeal cancer than in gynecological cancer. 18 F-FDG PET textural features were affected less by differences in the scanner and scan protocol than conventional and histogram features, possibly due to the resampling process using a medium bin width. Eight patients with gynecological cancer and 7 with oral cavity/oropharyngeal cancer underwent a whole-body 18 F-FDG PET/CT scan and regional PET/MR scan in one day. PET/MR scans were performed for 10 minutes in the list mode, and PET/CT and 0-2 min and 0-10 min PET/MR images were reconstructed. The standardized uptake value (SUV), tumor-to-muscle SUV ratio (TMR), skewness, kurtosis, entropy, homogeneity, short-run emphasis (SRE), and long-run emphasis (LRE) were compared between PET/CT, PET/MR 0-2 min , and PET/MR 0-10 min images.

  2. Improving permafrost distribution modelling using feature selection algorithms

    Science.gov (United States)

    Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

    2016-04-01

    overall operation. It operates by constructing a large collection of decorrelated classification trees, and then predicts the permafrost occurrence through a majority vote. With the so-called out-of-bag (OOB) error estimate, the classification of permafrost data can be validated as well as the contribution of each predictor can be assessed. The performances of compared permafrost distribution models (computed on independent testing sets) increased with the application of FS algorithms on the original dataset and irrelevant or redundant variables were removed. As a consequence, the process provided faster and more cost-effective predictors and a better understanding of the underlying structures residing in permafrost data. Our work demonstrates the usefulness of a feature selection step prior to applying a machine learning algorithm. In fact, permafrost predictors could be ranked not only based on their heuristic and subjective importance (expert knowledge), but also based on their statistical relevance in relation of the permafrost distribution.

  3. Task-relevant perceptual features can define categories in visual memory too.

    Science.gov (United States)

    Antonelli, Karla B; Williams, Carrick C

    2017-11-01

    Although Konkle, Brady, Alvarez, and Oliva (2010, Journal of Experimental Psychology: General, 139(3), 558) claim that visual long-term memory (VLTM) is organized on underlying conceptual, not perceptual, information, visual memory results from visual search tasks are not well explained by this theory. We hypothesized that when viewing an object, any task-relevant visual information is critical to the organizational structure of VLTM. In two experiments, we examined the organization of VLTM by measuring the amount of retroactive interference created by objects possessing different combinations of task-relevant features. Based on task instructions, only the conceptual category was task relevant or both the conceptual category and a perceptual object feature were task relevant. Findings indicated that when made task relevant, perceptual object feature information, along with conceptual category information, could affect memory organization for objects in VLTM. However, when perceptual object feature information was task irrelevant, it did not contribute to memory organization; instead, memory defaulted to being organized around conceptual category information. These findings support the theory that a task-defined organizational structure is created in VLTM based on the relevance of particular object features and information.

  4. Feature selection in classification of eye movements using electrooculography for activity recognition.

    Science.gov (United States)

    Mala, S; Latha, K

    2014-01-01

    Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

  5. Social validation of vocabulary selection: ensuring stakeholder relevance.

    Science.gov (United States)

    Bornman, Juan; Bryen, Diane Nelson

    2013-06-01

    The vocabulary needs of individuals who are unable to spell their messages continue to be of concern in the field of augmentative and alternative communication (AAC). Social validation of vocabulary selection has been suggested as one way to improve the effectiveness and relevance of service delivery in AAC. Despite increased emphasis on stakeholder accountability, social validation is not frequently used in AAC research. This paper describes an investigation of the social validity of a vocabulary set identified in earlier research. A previous study used stakeholder focus groups to identify vocabulary that could be used by South African adults who use AAC to disclose their experiences as victims of crime or abuse. Another study used this vocabulary to create communication boards for use by adults with complex communication needs. In this current project, 12 South African adults with complex communication needs who use AAC systems used a 5-point Likert scale to score the importance of each of the previously identified 57 vocabulary items. This two-step process of first using stakeholder focus groups to identify vocabulary, and then having literate persons who use AAC provide information on social validity of the vocabulary on behalf of their peers who are illiterate, appears to hold promise as a culturally relevant vocabulary selection approach for sensitive topics such as crime and abuse.

  6. Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

    Directory of Open Access Journals (Sweden)

    Jin-Jia Wang

    2015-01-01

    Full Text Available Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs. Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

  7. Feature Selection Criteria for Real Time EKF-SLAM Algorithm

    Directory of Open Access Journals (Sweden)

    Fernando Auat Cheein

    2010-02-01

    Full Text Available This paper presents a seletion procedure for environmet features for the correction stage of a SLAM (Simultaneous Localization and Mapping algorithm based on an Extended Kalman Filter (EKF. This approach decreases the computational time of the correction stage which allows for real and constant-time implementations of the SLAM. The selection procedure consists in chosing the features the SLAM system state covariance is more sensible to. The entire system is implemented on a mobile robot equipped with a range sensor laser. The features extracted from the environment correspond to lines and corners. Experimental results of the real time SLAM algorithm and an analysis of the processing-time consumed by the SLAM with the feature selection procedure proposed are shown. A comparison between the feature selection approach proposed and the classical sequential EKF-SLAM along with an entropy feature selection approach is also performed.

  8. The LAILAPS search engine: a feature model for relevance ranking in life science databases.

    Science.gov (United States)

    Lange, Matthias; Spies, Karl; Colmsee, Christian; Flemming, Steffen; Klapperstück, Matthias; Scholz, Uwe

    2010-03-25

    Efficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking. In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable. The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries. Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de.

  9. Features and selection of vascular access devices.

    Science.gov (United States)

    Sansivero, Gail Egan

    2010-05-01

    To review venous anatomy and physiology, discuss assessment parameters before vascular access device (VAD) placement, and review VAD options. Journal articles, personal experience. A number of VAD options are available in clinical practice. Access planning should include comprehensive assessment, with attention to patient participation in the planning and selection process. Careful consideration should be given to long-term access needs and preservation of access sites. Oncology nurses are uniquely suited to perform a key role in VAD planning and placement. With knowledge of infusion therapy, anatomy and physiology, device options, and community resources, nurses can be key leaders in preserving vascular access and improving the safety and comfort of infusion therapy. Copyright 2010 Elsevier Inc. All rights reserved.

  10. Online Feature Selection for Classifying Emphysema in HRCT Images

    Directory of Open Access Journals (Sweden)

    M. Prasad

    2008-06-01

    Full Text Available Feature subset selection, applied as a pre- processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In the classic formulation of the feature selection problem, it is assumed that all the features are available at the beginning. However, in many real world problems, there are scenarios where not all features are present initially and must be integrated as they become available. In such scenarios, online feature selection provides an efficient way to sort through a large space of features. It is in this context that we introduce online feature selection for the classification of emphysema, a smoking related disease that appears as low attenuation regions in High Resolution Computer Tomography (HRCT images. The technique was successfully evaluated on 61 HRCT scans and compared with different online feature selection approaches, including hill climbing, best first search, grafting, and correlation-based feature selection. The results were also compared against ldensity maskr, a standard approach used for emphysema detection in medical image analysis.

  11. Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

    Science.gov (United States)

    Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

    2018-02-01

    In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

  12. Feature selection using genetic algorithms for fetal heart rate analysis

    International Nuclear Information System (INIS)

    Xu, Liang; Redman, Christopher W G; Georgieva, Antoniya; Payne, Stephen J

    2014-01-01

    The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. (paper)

  13. Effective traffic features selection algorithm for cyber-attacks samples

    Science.gov (United States)

    Li, Yihong; Liu, Fangzheng; Du, Zhenyu

    2018-05-01

    By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

  14. Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

    Science.gov (United States)

    Xu, Jian-Wu; Suzuki, Kenji

    2014-03-01

    We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

  15. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    KAUST Repository

    Soufan, Othman

    2012-09-01

    Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.

  16. Feature-selective attention in healthy old age: a selective decline in selective attention?

    Science.gov (United States)

    Quigley, Cliodhna; Müller, Matthias M

    2014-02-12

    Deficient selection against irrelevant information has been proposed to underlie age-related cognitive decline. We recently reported evidence for maintained early sensory selection when older and younger adults used spatial selective attention to perform a challenging task. Here we explored age-related differences when spatial selection is not possible and feature-selective attention must be deployed. We additionally compared the integrity of feedforward processing by exploiting the well established phenomenon of suppression of visual cortical responses attributable to interstimulus competition. Electroencephalogram was measured while older and younger human adults responded to brief occurrences of coherent motion in an attended stimulus composed of randomly moving, orientation-defined, flickering bars. Attention was directed to horizontal or vertical bars by a pretrial cue, after which two orthogonally oriented, overlapping stimuli or a single stimulus were presented. Horizontal and vertical bars flickered at different frequencies and thereby elicited separable steady-state visual-evoked potentials, which were used to examine the effect of feature-based selection and the competitive influence of a second stimulus on ongoing visual processing. Age differences were found in feature-selective attentional modulation of visual responses: older adults did not show consistent modulation of magnitude or phase. In contrast, the suppressive effect of a second stimulus was robust and comparable in magnitude across age groups, suggesting that bottom-up processing of the current stimuli is essentially unchanged in healthy old age. Thus, it seems that visual processing per se is unchanged, but top-down attentional control is compromised in older adults when space cannot be used to guide selection.

  17. Features Selection for Skin Micro-Image Symptomatic Recognition

    Institute of Scientific and Technical Information of China (English)

    HUYue-li; CAOJia-lin; ZHAOQian; FENGXu

    2004-01-01

    Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92% using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

  18. Features Selection for Skin Micro-Image Symptomatic Recognition

    Institute of Scientific and Technical Information of China (English)

    HU Yue-li; CAO Jia-lin; ZHAO Qian; FENG Xu

    2004-01-01

    Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92 % using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

  19. Amygdala and auditory cortex exhibit distinct sensitivity to relevant acoustic features of auditory emotions.

    Science.gov (United States)

    Pannese, Alessia; Grandjean, Didier; Frühholz, Sascha

    2016-12-01

    Discriminating between auditory signals of different affective value is critical to successful social interaction. It is commonly held that acoustic decoding of such signals occurs in the auditory system, whereas affective decoding occurs in the amygdala. However, given that the amygdala receives direct subcortical projections that bypass the auditory cortex, it is possible that some acoustic decoding occurs in the amygdala as well, when the acoustic features are relevant for affective discrimination. We tested this hypothesis by combining functional neuroimaging with the neurophysiological phenomena of repetition suppression (RS) and repetition enhancement (RE) in human listeners. Our results show that both amygdala and auditory cortex responded differentially to physical voice features, suggesting that the amygdala and auditory cortex decode the affective quality of the voice not only by processing the emotional content from previously processed acoustic features, but also by processing the acoustic features themselves, when these are relevant to the identification of the voice's affective value. Specifically, we found that the auditory cortex is sensitive to spectral high-frequency voice cues when discriminating vocal anger from vocal fear and joy, whereas the amygdala is sensitive to vocal pitch when discriminating between negative vocal emotions (i.e., anger and fear). Vocal pitch is an instantaneously recognized voice feature, which is potentially transferred to the amygdala by direct subcortical projections. These results together provide evidence that, besides the auditory cortex, the amygdala too processes acoustic information, when this is relevant to the discrimination of auditory emotions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Hybrid feature selection for supporting lightweight intrusion detection systems

    Science.gov (United States)

    Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

    2017-08-01

    Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

  1. Joint Feature Selection and Classification for Multilabel Learning.

    Science.gov (United States)

    Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

    2018-03-01

    Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

  2. A Hybrid Feature Selection Approach for Arabic Documents Classification

    NARCIS (Netherlands)

    Habib, Mena Badieh; Sarhan, Ahmed A. E.; Salem, Abdel-Badeeh M.; Fayed, Zaki T.; Gharib, Tarek F.

    Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge number of features. Feature selection tries to

  3. Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

    Directory of Open Access Journals (Sweden)

    Atiyeh Mortazavi

    2016-01-01

    Full Text Available High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

  4. Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness

    Energy Technology Data Exchange (ETDEWEB)

    Pon, R K; Cardenas, A F; Buttler, D J

    2007-09-19

    The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifier with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.

  5. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

    Directory of Open Access Journals (Sweden)

    Jaesung Lee

    2016-11-01

    Full Text Available Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

  6. Feature Extraction and Selection Strategies for Automated Target Recognition

    Science.gov (United States)

    Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

    2010-01-01

    Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

  7. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

    Science.gov (United States)

    Li, Baopu; Meng, Max Q-H

    2012-05-01

    Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

  8. Intraductal papillary mucinous neoplasms of the pancreas: reporting clinically relevant features.

    Science.gov (United States)

    Del Chiaro, Marco; Verbeke, Caroline

    2017-05-01

    Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas can exhibit a wide spectrum of macroscopic and microscopic appearances. This not only causes occasional difficulties for the reporting pathologist in distinguishing these tumours from other lesions, but is also relevant clinically. As evidence accumulates, it becomes clear that multiple macroscopic and histological features of these neoplasms are relevant to the risk for malignant transformation and, consequently, of prime importance for clinical patient management. The need for detailed reporting is therefore increasing. This review discusses the panoply of gross and microscopic features of IPMN as well as the recommendations from recent consensus meetings regarding the pathology reporting on this tumour entity. © 2016 John Wiley & Sons Ltd.

  9. Feature selection and nearest centroid classification for protein mass spectrometry

    Directory of Open Access Journals (Sweden)

    Levner Ilya

    2005-03-01

    Full Text Available Abstract Background The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. Results This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. Conclusion This study tested a number of popular feature

  10. Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.

    Science.gov (United States)

    Ghosh, Samiran; Wang, Yazhen

    2015-02-01

    The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few observations which lie close to the boundary of the classifier function. However when the number of observations are not very large (small n ) but the number of dimensions/features are large (large p ), then it is not necessary that all available features are of equal importance in the classification context. Possible selection of an useful fraction of the available features may result in huge data compression. In this paper we propose an algorithmic approach by means of which such an optimal set of features could be selected. In short, we reverse the traditional sequential observation selection strategy of SVM to that of sequential feature selection. To achieve this we have modified the solution proposed by Zhu and Hastie (2005) in the context of import vector machine (IVM), to select an optimal sub-dimensional model to build the final classifier with sufficient accuracy.

  11. MememxGATE: Unearthing Latent Content Features for Improved Search and Relevancy Ranking Across Scientific Literature

    Science.gov (United States)

    Wilson, B. D.; McGibbney, L. J.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Whitehall, K. D.

    2015-12-01

    Quantifying scientific relevancy is of increasing importance to NASA and the research community. Scientific relevancy may be defined by mapping the impacts of a particular NASA mission, instrument, and/or retrieved variables to disciplines such as climate predictions, natural hazards detection and mitigation processes, education, and scientific discoveries. Related to relevancy, is the ability to expose data with similar attributes. This in turn depends upon the ability for us to extract latent, implicit document features from scientific data and resources and make them explicit, accessible and useable for search activities amongst others. This paper presents MemexGATE; a server side application, command line interface and computing environment for running large scale metadata extraction, general architecture text engineering, document classification and indexing tasks over document resources such as social media streams, scientific literature archives, legal documentation, etc. This work builds on existing experiences using MemexGATE (funded, developed and validated through the DARPA Memex Progrjam PI Mattmann) for extracting and leveraging latent content features from document resources within the Materials Research domain. We extend the software functionality capability to the domain of scientific literature with emphasis on the expansion of gazetteer lists, named entity rules, natural language construct labeling (e.g. synonym, antonym, hyponym, etc.) efforts to enable extraction of latent content features from data hosted by wide variety of scientific literature vendors (AGU Meeting Abstract Database, Springer, Wiley Online, Elsevier, etc.) hosting earth science literature. Such literature makes both implicit and explicit references to NASA datasets and relationships between such concepts stored across EOSDIS DAAC's hence we envisage that a significant part of this effort will also include development and understanding of relevancy signals which can ultimately

  12. Tensor-based Multi-view Feature Selection with Applications to Brain Diseases

    Science.gov (United States)

    Cao, Bokai; He, Lifang; Kong, Xiangnan; Yu, Philip S.; Hao, Zhifeng; Ragin, Ann B.

    2015-01-01

    In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series of medical examinations are documented for each subject, including clinical, imaging, immunologic, serologic and cognitive measures which are obtained from multiple sources. Specifically, for brain diagnosis, we can have different quantitative analysis which can be seen as different feature subsets of a subject. It is desirable to combine all these features in an effective way for disease diagnosis. However, some measurements from less relevant medical examinations can introduce irrelevant information which can even be exaggerated after view combinations. Feature selection should therefore be incorporated in the process of multi-view learning. In this paper, we explore tensor product to bring different views together in a joint space, and present a dual method of tensor-based multi-view feature selection (dual-Tmfs) based on the idea of support vector machine recursive feature elimination. Experiments conducted on datasets derived from neurological disorder demonstrate the features selected by our proposed method yield better classification performance and are relevant to disease diagnosis. PMID:25937823

  13. Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech

    Directory of Open Access Journals (Sweden)

    Santiago Planet

    2012-09-01

    Full Text Available The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Naïve-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set.

  14. Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

    Science.gov (United States)

    Downer, Joshua D; Rapone, Brittany; Verhein, Jessica; O'Connor, Kevin N; Sutter, Mitchell L

    2017-05-24

    Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations ( r noise ) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on r noise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in r noise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations ( r noise ) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on r noise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning

  15. Longitudinal MRI assessment: the identification of relevant features in the development of Posterior Fossa Syndrome in children

    Science.gov (United States)

    Spiteri, M.; Lewis, E.; Windridge, D.; Avula, S.

    2015-03-01

    Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in lobes within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children's Hospital, Liver- pool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children's Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally.

  16. [Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

    Science.gov (United States)

    Zhou, Jinzhi; Tang, Xiaofang

    2015-08-01

    In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

  17. Effective Feature Selection for Classification of Promoter Sequences.

    Directory of Open Access Journals (Sweden)

    Kouser K

    Full Text Available Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine, KNN (K Nearest Neighbor and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.

  18. Pairwise Constraint-Guided Sparse Learning for Feature Selection.

    Science.gov (United States)

    Liu, Mingxia; Zhang, Daoqiang

    2016-01-01

    Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

  19. Simultaneous feature selection and classification via Minimax Probability Machine

    Directory of Open Access Journals (Sweden)

    Liming Yang

    2010-12-01

    Full Text Available This paper presents a novel method for simultaneous feature selection and classification by incorporating a robust L1-norm into the objective function of Minimax Probability Machine (MPM. A fractional programming framework is derived by using a bound on the misclassification error involving the mean and covariance of the data. Furthermore, the problems are solved by the Quadratic Interpolation method. Experiments show that our methods can select fewer features to improve the generalization compared to MPM, which illustrates the effectiveness of the proposed algorithms.

  20. Feature Selection for Audio Surveillance in Urban Environment

    Directory of Open Access Journals (Sweden)

    KIKTOVA Eva

    2014-05-01

    Full Text Available This paper presents the work leading to the acoustic event detection system, which is designed to recognize two types of acoustic events (shot and breaking glass in urban environment. For this purpose, a huge front-end processing was performed for the effective parametric representation of an input sound. MFCC features and features computed during their extraction (MELSPEC and FBANK, then MPEG-7 audio descriptors and other temporal and spectral characteristics were extracted. High dimensional feature sets were created and in the next phase reduced by the mutual information based selection algorithms. Hidden Markov Model based classifier was applied and evaluated by the Viterbi decoding algorithm. Thus very effective feature sets were identified and also the less important features were found.

  1. Feature diagnosticity and task context shape activity in human scene-selective cortex.

    Science.gov (United States)

    Lowe, Matthew X; Gallivan, Jason P; Ferber, Susanne; Cant, Jonathan S

    2016-01-15

    Scenes are constructed from multiple visual features, yet previous research investigating scene processing has often focused on the contributions of single features in isolation. In the real world, features rarely exist independently of one another and likely converge to inform scene identity in unique ways. Here, we utilize fMRI and pattern classification techniques to examine the interactions between task context (i.e., attend to diagnostic global scene features; texture or layout) and high-level scene attributes (content and spatial boundary) to test the novel hypothesis that scene-selective cortex represents multiple visual features, the importance of which varies according to their diagnostic relevance across scene categories and task demands. Our results show for the first time that scene representations are driven by interactions between multiple visual features and high-level scene attributes. Specifically, univariate analysis of scene-selective cortex revealed that task context and feature diagnosticity shape activity differentially across scene categories. Examination using multivariate decoding methods revealed results consistent with univariate findings, but also evidence for an interaction between high-level scene attributes and diagnostic visual features within scene categories. Critically, these findings suggest visual feature representations are not distributed uniformly across scene categories but are shaped by task context and feature diagnosticity. Thus, we propose that scene-selective cortex constructs a flexible representation of the environment by integrating multiple diagnostically relevant visual features, the nature of which varies according to the particular scene being perceived and the goals of the observer. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Different cortical mechanisms for spatial vs. feature-based attentional selection in visual working memory

    Directory of Open Access Journals (Sweden)

    Anna Heuer

    2016-08-01

    Full Text Available The limited capacity of visual working memory necessitates attentional mechanisms that selectively update and maintain only the most task-relevant content. Psychophysical experiments have shown that the retroactive selection of memory content can be based on visual properties such as location or shape, but the neural basis for such differential selection is unknown. For example, it is not known if there are different cortical modules specialized for spatial versus feature-based mnemonic attention, in the same way that has been demonstrated for attention to perceptual input. Here, we used transcranial magnetic stimulation (TMS to identify areas in human parietal and occipital cortex involved in the selection of objects from memory based on cues to their location (spatial information or their shape (featural information. We found that TMS over the supramarginal gyrus (SMG selectively facilitated spatial selection, whereas TMS over the lateral occipital cortex selectively enhanced feature-based selection for remembered objects in the contralateral visual field. Thus, different cortical regions are responsible for spatial vs. feature-based selection of working memory representations. Since the same regions are involved in attention to external events, these new findings indicate overlapping mechanisms for attentional control over perceptual input and mnemonic representations.

  3. Feature selection gait-based gender classification under different circumstances

    Science.gov (United States)

    Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

    2014-05-01

    This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

  4. Emotion of Physiological Signals Classification Based on TS Feature Selection

    Institute of Scientific and Technical Information of China (English)

    Wang Yujing; Mo Jianlin

    2015-01-01

    This paper propose a method of TS-MLP about emotion recognition of physiological signal.It can recognize emotion successfully by Tabu search which selects features of emotion’s physiological signals and multilayer perceptron that is used to classify emotion.Simulation shows that it has achieved good emotion classification performance.

  5. Orthogonal feature selection method. [For preprocessing of man spectral data

    Energy Technology Data Exchange (ETDEWEB)

    Kowalski, B R [Univ. of Washington, Seattle; Bender, C F

    1976-01-01

    A new method of preprocessing spectral data for extraction of molecular structural information is desired. This SELECT method generates orthogonal features that are important for classification purposes and that also retain their identity to the original measurements. A brief introduction to chemical pattern recognition is presented. A brief description of the method and an application to mass spectral data analysis follow. (BLM)

  6. Technical Evaluation Report 27: Educational Wikis: Features and selection criteria

    Directory of Open Access Journals (Sweden)

    Jim Rudolph

    2004-04-01

    Full Text Available This report discusses the educational uses of the ‘wiki,’ an increasingly popular approach to online community development. Wikis are defined and compared with ‘blogging’ methods; characteristics of major wiki engines are described; and wiki features and selection criteria are examined.

  7. Feature selection for high-dimensional integrated data

    KAUST Repository

    Zheng, Charles; Schwartz, Scott; Chapkin, Robert S.; Carroll, Raymond J.; Ivanov, Ivan

    2012-01-01

    Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.

  8. Feature selection for high-dimensional integrated data

    KAUST Repository

    Zheng, Charles

    2012-04-26

    Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.

  9. Functional connectivity supporting the selective maintenance of feature-location binding in visual working memory

    Directory of Open Access Journals (Sweden)

    Sachiko eTakahama

    2014-06-01

    Full Text Available Information on an object’s features bound to its location is very important for maintaining object representations in visual working memory. Interactions with dynamic multi-dimensional objects in an external environment require complex cognitive control, including the selective maintenance of feature-location binding. Here, we used event-related functional magnetic resonance imaging to investigate brain activity and functional connectivity related to the maintenance of complex feature-location binding. Participants were required to detect task-relevant changes in feature-location binding between objects defined by color, orientation, and location. We compared a complex binding task requiring complex feature-location binding (color-orientation-location with a simple binding task in which simple feature-location binding, such as color-location, was task-relevant and the other feature was task-irrelevant. Univariate analyses showed that the dorsolateral prefrontal cortex (DLPFC, hippocampus, and frontoparietal network were activated during the maintenance of complex feature-location binding. Functional connectivity analyses indicated cooperation between the inferior precentral sulcus (infPreCS, DLPFC, and hippocampus during the maintenance of complex feature-location binding. In contrast, the connectivity for the spatial updating of simple feature-location binding determined by reanalyzing the data from Takahama et al. (2010 demonstrated that the superior parietal lobule (SPL cooperated with the DLPFC and hippocampus. These results suggest that the connectivity for complex feature-location binding does not simply reflect general memory load and that the DLPFC and hippocampus flexibly modulate the dorsal frontoparietal network, depending on the task requirements, with the infPreCS involved in the maintenance of complex feature-location binding and the SPL involved in the spatial updating of simple feature-location binding.

  10. Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.

    Directory of Open Access Journals (Sweden)

    Bineng Zhong

    Full Text Available To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection for an object is one of the key issues. Typically, a common assumption used in visual tracking is that the raw video sequences are clear, while real-world data is with significant noise and irrelevant patterns. Consequently, the learned features may be not all relevant and noisy. To address this problem, we propose a novel visual tracking method via a point-wise gated convolutional deep network (CPGDN that jointly performs the feature learning and feature selection in a unified framework. The proposed method performs dynamic feature selection on raw features through a gating mechanism. Therefore, the proposed method can adaptively focus on the task-relevant patterns (i.e., a target object, while ignoring the task-irrelevant patterns (i.e., the surrounding background of a target object. Specifically, inspired by transfer learning, we firstly pre-train an object appearance model offline to learn generic image features and then transfer rich feature hierarchies from an offline pre-trained CPGDN into online tracking. In online tracking, the pre-trained CPGDN model is fine-tuned to adapt to the tracking specific objects. Finally, to alleviate the tracker drifting problem, inspired by an observation that a visual target should be an object rather than not, we combine an edge box-based object proposal method to further improve the tracking accuracy. Extensive evaluation on the widely used CVPR2013 tracking benchmark validates the robustness and effectiveness of the proposed method.

  11. Linear feature selection in texture analysis - A PLS based method

    DEFF Research Database (Denmark)

    Marques, Joselene; Igel, Christian; Lillholm, Martin

    2013-01-01

    We present a texture analysis methodology that combined uncommitted machine-learning techniques and partial least square (PLS) in a fully automatic framework. Our approach introduces a robust PLS-based dimensionality reduction (DR) step to specifically address outliers and high-dimensional feature...... and considering all CV groups, the methods selected 36 % of the original features available. The diagnosis evaluation reached a generalization area-under-the-ROC curve of 0.92, which was higher than established cartilage-based markers known to relate to OA diagnosis....

  12. New Hybrid Features Selection Method: A Case Study on Websites Phishing

    Directory of Open Access Journals (Sweden)

    Khairan D. Rajab

    2017-01-01

    Full Text Available Phishing is one of the serious web threats that involves mimicking authenticated websites to deceive users in order to obtain their financial information. Phishing has caused financial damage to the different online stakeholders. It is massive in the magnitude of hundreds of millions; hence it is essential to minimize this risk. Classifying websites into “phishy” and legitimate types is a primary task in data mining that security experts and decision makers are hoping to improve particularly with respect to the detection rate and reliability of the results. One way to ensure the reliability of the results and to enhance performance is to identify a set of related features early on so the data dimensionality reduces and irrelevant features are discarded. To increase reliability of preprocessing, this article proposes a new feature selection method that combines the scores of multiple known methods to minimize discrepancies in feature selection results. The proposed method has been applied to the problem of website phishing classification to show its pros and cons in identifying relevant features. Results against a security dataset reveal that the proposed preprocessing method was able to derive new features datasets which when mined generate high competitive classifiers with reference to detection rate when compared to results obtained from other features selection methods.

  13. Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

    Directory of Open Access Journals (Sweden)

    Arif Mehmood

    2017-01-01

    Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

  14. Task relevance modulates the cortical representation of feature conjunctions in the target template.

    Science.gov (United States)

    Reeder, Reshanne R; Hanke, Michael; Pollmann, Stefan

    2017-07-03

    Little is known about the cortical regions involved in representing task-related content in preparation for visual task performance. Here we used representational similarity analysis (RSA) to investigate the BOLD response pattern similarity between task relevant and task irrelevant feature dimensions during conjunction viewing and target template maintenance prior to visual search. Subjects were cued to search for a spatial frequency (SF) or orientation of a Gabor grating and we measured BOLD signal during cue and delay periods before the onset of a search display. RSA of delay period activity revealed that widespread regions in frontal, posterior parietal, and occipitotemporal cortices showed general representational differences between task relevant and task irrelevant dimensions (e.g., orientation vs. SF). In contrast, RSA of cue period activity revealed sensory-related representational differences between cue images (regardless of task) at the occipital pole and additionally in the frontal pole. Our data show that task and sensory information are represented differently during viewing and during target template maintenance, and that task relevance modulates the representation of visual information across the cortex.

  15. A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data

    KAUST Repository

    Abusamra, Heba

    2013-01-01

    Different experiments have been applied to compare the performance of the classification methods with and without performing feature selection. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.

  16. Toward optimal feature selection using ranking methods and classification algorithms

    Directory of Open Access Journals (Sweden)

    Novaković Jasmina

    2011-01-01

    Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.

  17. An opinion formation based binary optimization approach for feature selection

    Science.gov (United States)

    Hamedmoghadam, Homayoun; Jalili, Mahdi; Yu, Xinghuo

    2018-02-01

    This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.

  18. Benefit salience and consumers' selective attention to product features

    OpenAIRE

    Ratneshwar, S; Warlop, Luk; Mick, DG; Seeger, G

    1997-01-01

    Although attention is a key construct in models of marketing communication and consumer choice, its selective nature has rarely been examined in common time-pressured conditions. We focus on the role of benefit salience, that is, the readiness with which particular benefits are brought to mind by consumers in relation to a given product category. Study I demonstrated that when product feature information was presented rapidly, individuals for whom the benefit of personalised customer service ...

  19. Fast Branch & Bound algorithms for optimal feature selection

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Pudil, Pavel; Kittler, J.

    2004-01-01

    Roč. 26, č. 7 (2004), s. 900-912 ISSN 0162-8828 R&D Projects: GA ČR GA402/02/1271; GA ČR GA402/03/1310; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : subset search * feature selection * search tree Subject RIV: BD - Theory of Information Impact factor: 4.352, year: 2004

  20. DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

    KAUST Repository

    Soufan, Othman

    2015-02-26

    Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem\\'s dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filteringmethods thatmay be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.

  1. DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

    KAUST Repository

    Soufan, Othman; Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

    2015-01-01

    Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem's dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filteringmethods thatmay be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.

  2. Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

    Science.gov (United States)

    Padmanaban, Subash; Baker, Justin; Greger, Bradley

    2018-01-01

    Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements—similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the feature vector supplied to the SVM. Main results: The average decoding accuracy for multi-unit features and single-unit features using Mutual Information Maximization (MIM) across 47 sessions was 96.74 ± 3.5% and 97.65 ± 3.36% respectively. The reduction in decoding accuracy between using 100% of the features and 10% of features based on MIM was 45.56% (from 93.7 to 51.09%) and 4.75% (from 95.32 to 90.79%) for multi-unit and single-unit features respectively. MIM had best performance compared to other feature selection methods. Significance: These results suggest improved decoding performance can be achieved by using optimally selected features. The results based on clinically relevant performance metrics also suggest that the decoding

  3. Divided attention selectively impairs memory for self-relevant information.

    Science.gov (United States)

    Turk, David J; Brady-van den Bos, Mirjam; Collard, Philip; Gillespie-Smith, Karri; Conway, Martin A; Cunningham, Sheila J

    2013-05-01

    Information that is relevant to oneself tends to be remembered more than information that relates to other people, but the role of attention in eliciting this "self-reference effect" is unclear. In the present study, we assessed the importance of attention in self-referential encoding using an ownership paradigm, which required participants to encode items under conditions of imagined ownership by themselves or by another person. Previous work has established that this paradigm elicits a robust self-reference effect, with more "self-owned" items being remembered than "other-owned" items. Access to attentional resources was manipulated using divided-attention tasks at encoding. A significant self-reference effect emerged under full-attention conditions and was related to an increase in episodic recollection for self-owned items, but dividing attention eliminated this memory advantage. These findings are discussed in relation to the nature of self-referential cognition and the importance of attentional resources at encoding in the manifestation of the self-reference effect in memory.

  4. An analysis of feature relevance in the classification of astronomical transients with machine learning methods

    Science.gov (United States)

    D'Isanto, A.; Cavuoti, S.; Brescia, M.; Donalek, C.; Longo, G.; Riccio, G.; Djorgovski, S. G.

    2016-04-01

    The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae.

  5. A ROC-based feature selection method for computer-aided detection and diagnosis

    Science.gov (United States)

    Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

    2014-03-01

    Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

  6. Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

    Science.gov (United States)

    Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R.; Barman, Ishan; Kumar Gundawar, Manoj

    2015-01-01

    Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations. PMID:26286630

  7. Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

    Science.gov (United States)

    Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

    2017-01-01

    Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

  8. Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

    Science.gov (United States)

    Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

    2017-01-01

    Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

  9. Information Theory for Gabor Feature Selection for Face Recognition

    Directory of Open Access Journals (Sweden)

    Shen Linlin

    2006-01-01

    Full Text Available A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004, our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

  10. Information Theory for Gabor Feature Selection for Face Recognition

    Science.gov (United States)

    Shen, Linlin; Bai, Li

    2006-12-01

    A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

  11. Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

    Science.gov (United States)

    Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

    2014-08-01

    Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

  12. An enhanced PSO-DEFS based feature selection with biometric authentication for identification of diabetic retinopathy

    Directory of Open Access Journals (Sweden)

    Umarani Balakrishnan

    2016-11-01

    Full Text Available Recently, automatic diagnosis of diabetic retinopathy (DR from the retinal image is the most significant research topic in the medical applications. Diabetic macular edema (DME is the major reason for the loss of vision in patients suffering from DR. Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities. Many techniques are developed to diagnose the DR. The major drawbacks of the existing techniques are low accuracy and high time complexity. To overcome these issues, this paper proposes an enhanced particle swarm optimization-differential evolution feature selection (PSO-DEFS based feature selection approach with biometric authentication for the identification of DR. Initially, a hybrid median filter (HMF is used for pre-processing the input images. Then, the pre-processed images are embedded with each other by using least significant bit (LSB for authentication purpose. Simultaneously, the image features are extracted using convoluted local tetra pattern (CLTrP and Tamura features. Feature selection is performed using PSO-DEFS and PSO-gravitational search algorithm (PSO-GSA to reduce time complexity. Based on some performance metrics, the PSO-DEFS is chosen as a better choice for feature selection. The feature selection is performed based on the fitness value. A multi-relevance vector machine (M-RVM is introduced to classify the 13 normal and 62 abnormal images among 75 images from 60 patients. Finally, the DR patients are further classified by M-RVM. The experimental results exhibit that the proposed approach achieves better accuracy, sensitivity, and specificity than the existing techniques.

  13. [Psychocardiology: clinically relevant recommendations regarding selected cardiovascular diseases].

    Science.gov (United States)

    Albus, C; Ladwig, K-H; Herrmann-Lingen, C

    2014-03-01

    Psychosocial risk factors (work stress, low socioeconomic status, impaired social support, anger, anxiety and depression), certain personality traits (e.g. hostility) and post-traumatic stress disorders may negatively influence the incidence and course of multiple cardiovascular disease conditions. Systematic screening for these factors may help to adequately assess the psychosocial risk pattern of a given patient and may also contribute to the treatment of these patients. Recommendations for treatment are based on current guidelines. The physician-patient interaction should basically follow the principle of a patient centered communication and should gender and age specific aspects into consideration. Integrated biopsychosocial care is an effective, low threshold option to treat psycho-social risk factors and should be offered on a regular basis. Patients with high blood pressure may profit from relaxation programs and biofeedback procedures (however with moderate success). An individually adjusted multimodal treatment strategy should be offered to patients with coronary heart disease, heart failure and after heart surgery. It may incorporate educational tools, exercise therapy, motivational modules, relaxation and stress management programs. In case of affective comorbidity, psychotherapy may be indicated. Anti-depressant pharmacotherapy with selective serotonin reuptake inhibitors (SSRIs) in the first line should only be offered to patients with at least moderate severe depressive episodes. Psychotherapy and SSRIs, particularly sertraline, have been proven to be safe and effective with regard to improvements of the patient's quality of life. A prognostic benefit has not been clearly proven so far. Patients with an implanted cardioverter/defibrillator (ICD) should receive psychosocial support on a regular basis. Concomitant psychotherapy and/or psychopharmacotherapy (SSRIs) should be offered in case of a severe mental comorbidity. Generally, tricyclic

  14. License Application Design Selection Feature Report: Aging and Blending

    International Nuclear Information System (INIS)

    Coltoni, B.; Anderson, M.J.

    1999-01-01

    The purpose of this document is to evaluate the concepts of Aging and Blending for waste sent to the Monitored Geologic Repository (MGR). These design features are based on pre-emplacement treatment of the waste stream. The envelope of the analysis has been performed under the direction of the License Application Design Selection Team (LADST), which advocated utilizing the Viability Assessment (VA) repository design (DOE 1998c) as the basis. Therefore, this evaluation attempts to modify the VA design only to the extent that Aging and Blending can be accomplished. This modified VA design will be contrasted to the VA Design and the difference in design, costs, and performance will be presented

  15. Notes on the evolution of feature selection methodology

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Novovičová, Jana; Pudil, Pavel

    2007-01-01

    Roč. 43, č. 5 (2007), s. 713-730 ISSN 0023-5954 R&D Projects: GA ČR GA102/07/1594; GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection * branch and bound * sequential search * mixture model Subject RIV: IN - Informatics, Computer Science Impact factor: 0.552, year: 2007

  16. Conditional Mutual Information Based Feature Selection for Classification Task

    Czech Academy of Sciences Publication Activity Database

    Novovičová, Jana; Somol, Petr; Haindl, Michal; Pudil, Pavel

    2007-01-01

    Roč. 45, č. 4756 (2007), s. 417-426 ISSN 0302-9743 R&D Projects: GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Pattern classification * feature selection * conditional mutual information * text categorization Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.402, year: 2005

  17. Feature Extraction

    CERN Document Server

    CERN. Geneva

    2015-01-01

    Feature selection and reduction are key to robust multivariate analyses. In this talk I will focus on pros and cons of various variable selection methods and focus on those that are most relevant in the context of HEP.

  18. An Ensemble Method with Integration of Feature Selection and Classifier Selection to Detect the Landslides

    Science.gov (United States)

    Zhongqin, G.; Chen, Y.

    2017-12-01

    Abstract Quickly identify the spatial distribution of landslides automatically is essential for the prevention, mitigation and assessment of the landslide hazard. It's still a challenging job owing to the complicated characteristics and vague boundary of the landslide areas on the image. The high resolution remote sensing image has multi-scales, complex spatial distribution and abundant features, the object-oriented image classification methods can make full use of the above information and thus effectively detect the landslides after the hazard happened. In this research we present a new semi-supervised workflow, taking advantages of recent object-oriented image analysis and machine learning algorithms to quick locate the different origins of landslides of some areas on the southwest part of China. Besides a sequence of image segmentation, feature selection, object classification and error test, this workflow ensemble the feature selection and classifier selection. The feature this study utilized were normalized difference vegetation index (NDVI) change, textural feature derived from the gray level co-occurrence matrices (GLCM), spectral feature and etc. The improvement of this study shows this algorithm significantly removes some redundant feature and the classifiers get fully used. All these improvements lead to a higher accuracy on the determination of the shape of landslides on the high resolution remote sensing image, in particular the flexibility aimed at different kinds of landslides.

  19. Structural features of subtype-selective EP receptor modulators.

    Science.gov (United States)

    Markovič, Tijana; Jakopin, Žiga; Dolenc, Marija Sollner; Mlinarič-Raščan, Irena

    2017-01-01

    Prostaglandin E2 is a potent endogenous molecule that binds to four different G-protein-coupled receptors: EP1-4. Each of these receptors is a valuable drug target, with distinct tissue localisation and signalling pathways. We review the structural features of EP modulators required for subtype-selective activity, as well as the structural requirements for improved pharmacokinetic parameters. Novel EP receptor subtype selective agonists and antagonists appear to be valuable drug candidates in the therapy of many pathophysiological states, including ulcerative colitis, glaucoma, bone healing, B cell lymphoma, neurological diseases, among others, which have been studied in vitro, in vivo and in early phase clinical trials. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

    Directory of Open Access Journals (Sweden)

    Kuan-Cheng Lin

    2015-01-01

    Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

  1. Features of mechanical snubbers and the method of selection

    International Nuclear Information System (INIS)

    Sunakoda, Katsuaki

    1978-01-01

    In the oil snubbers used in the high radiation environment of nuclear power stations, gas generation from oil and the deterioration of rubber material for sealing occur due to radiation damage, therefore periodical inspection and replacement are required during operation. The mechanical snubbers developed as aseismatic supporters in place of oil snubbers have entered the stage of practical use, and are made by two companies in USA and a company in Japan. Their features as compared with oil snubbers are as follows. The cost and time required for the maintenance were made as small as possible because the increase of the service life of mechanical components can be expected. The temperature dependence of mechanical snubbers is small. The matters demanding attention in the maintenance are the secular change of lubricating oil and the effect of radiation, and the rust prevention of ball screw bearings. These problems are being studied by Power Reactor and Nuclear Fuel Development Corp. for the fast prototype reactor Monju. The structural feature is to convert the thrust movement of equipments and pipings due to thermal expansion and contraction or earthquakes into rotating motion, using ball screws. The features and the construction of SMS type mechanical snubbers, the test and inspection prior to their shipping, the method of selection, and the method of handling them in actual places are explained. (Kako, I.)

  2. Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making.

    Science.gov (United States)

    Scholl, Jacqueline; Klein-Flügge, Miriam

    2017-09-28

    Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision-making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision-making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost-benefit decision-making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real-life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  3. [Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

    Science.gov (United States)

    Li, Jing; Hong, Wenxue

    2014-12-01

    The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

  4. Identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis by using the Delphi Technique

    Science.gov (United States)

    Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.

    2018-02-01

    This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.

  5. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

    Directory of Open Access Journals (Sweden)

    Gabere MN

    2016-06-01

    Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1

  6. Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

    Directory of Open Access Journals (Sweden)

    Faimison Porto

    2016-12-01

    Full Text Available The defect prediction models can be a good tool on organizing the project's test resources. The models can be constructed with two main goals: 1 to classify the software parts - defective or not; or 2 to rank the most defective parts in a decreasing order. However, not all companies maintain an appropriate set of historical defect data. In this case, a company can build an appropriate dataset from known external projects - called Cross-project Defect Prediction (CPDP. The CPDP models, however, present low prediction performances due to the heterogeneity of data. Recently, Instance Filtering methods were proposed in order to reduce this heterogeneity by selecting the most similar instances from the training dataset. Originally, the similarity is calculated based on all the available dataset features (or independent variables. We propose that using only the most relevant features on the similarity calculation can result in more accurate filtered datasets and better prediction performances. In this study we extend our previous work. We analyse both prediction goals - Classification and Ranking. We present an empirical evaluation of 41 different methods by associating Instance Filtering methods with Feature Selection methods. We used 36 versions of 11 open source projects on experiments. The results show similar evidences for both prediction goals. First, the defect prediction performance of CPDP models can be improved by associating Feature Selection and Instance Filtering. Second, no evaluated method presented general better performances. Indeed, the most appropriate method can vary according to the characteristics of the project being predicted.

  7. Effects of changing canopy directional reflectance on feature selection

    Science.gov (United States)

    Smith, J. A.; Oliver, R. E.; Kilpela, O. E.

    1973-01-01

    The use of a Monte Carlo model for generating sample directional reflectance data for two simplified target canopies at two different solar positions is reported. Successive iterations through the model permit the calculation of a mean vector and covariance matrix for canopy reflectance for varied sensor view angles. These data may then be used to calculate the divergence between the target distributions for various wavelength combinations and for these view angles. Results of a feature selection analysis indicate that different sets of wavelengths are optimum for target discrimination depending on sensor view angle and that the targets may be more easily discriminated for some scan angles than others. The time-varying behavior of these results is also pointed out.

  8. Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data

    International Nuclear Information System (INIS)

    Balabin, Roman M.; Smirnov, Sergey V.

    2011-01-01

    During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm -1 ) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic

  9. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

    Directory of Open Access Journals (Sweden)

    Pradipta Maji

    Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

  10. Electricity market price spike analysis by a hybrid data model and feature selection technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2010-01-01

    In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

  11. Influence of surface features of hydroxyapatite on the adsorption of proteins relevant to bone regeneration.

    Science.gov (United States)

    Fernández-Montes Moraleda, Belén; San Román, Julio; Rodríguez-Lorenzo, Luís M

    2013-08-01

    Protein-surface interaction may determine the success or failure of an implanted device. Not much attention have been paid to the specific surface parametes of hydroxyapatite (OHAp) that modulates and determines the formation and potential activity of the layer of proteins that is first formed when the material get in contact with the host tissue. the influence of specific surface area (SSA), crystallite size (CS) and particle size (PS) of OHAp on the adsorption of proteins relevant for bone regeneration is evaluated in this article. OHAp have been prepared by a wet chemical reaction of Ca(OH)2 with H3PO4. One set of reactions included poly acrylic acid in the reactant solution to modify the properties of the powder. Fibrinogen (Fg) Fraction I, type I: from Human plasma, (67% Protein), and Fibronectin (Fn) from Human plasma were selected to perform the adsorption experiments. The analysis of protein adsorption was carried out by UV/Vis spectrometry. A lower SSA and a different aspect ratio are obtained when the acrylic acid is included in the reaction badge. The deconvolution of the amide I band on the Raman spectra of free and adsorbed proteins reveals that the interaction apatite-protein happens through the carboxylate groups of the proteins. The combined analysis of CS, SSA and PS should be considered on the design of OHAp materials intended to interact with proteins. Copyright © 2013 Wiley Periodicals, Inc.

  12. Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis.

    Directory of Open Access Journals (Sweden)

    Lu-Lu Zheng

    Full Text Available Pyrrolidone carboxylic acid (PCA is formed during a common post-translational modification (PTM of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR and incremental feature selection (IFS. We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.

  13. MET overexpression, gene amplification and relevant clinicopathological features in gastric adenocarcinoma.

    Science.gov (United States)

    Zhang, Jing; Guo, Lei; Liu, Xiuyun; Li, Wenbin; Ying, Jianming

    2017-02-07

    This study was conducted to investigate the expression of MET in Chinese gastric adenocarcinoma cohort, the correlation between MET overexpression and clinical pathological features, HER2 expression and MET gene amplification. A total of 816 gastric adenocarcinoma patients were included and MET and HER2 immunohistochemical (IHC) staining were performed. IHC and dual-color silver in situ hybridization analysis were performed in the tissue microarrays, constructed from the 240 patients who were randomly selected. MET overexpression (IHC 3+) was observed in 6.0% (49/816) of the cohort. MET overexpression rate was higher in patients with poor prognostic factors, such as clinical stages III/IV (p =0.012) and pathologic stages T3/T4 (p =0.027). The HER2 overexpression (IHC 3+) rate was 8.8% (72/816) and MET overexpression rate was higher in HER2 positive patients (9.7%, 7/72). A high concordance rate (94.6%) between MET overexpression and gene amplification was demonstrated. Therefore, MET overexpression could serve as a prognostic biomarker and a potential therapeutic target for gastric cancer.

  14. Enhancement web proxy cache performance using Wrapper Feature Selection methods with NB and J48

    Science.gov (United States)

    Mahmoud Al-Qudah, Dua'a.; Funke Olanrewaju, Rashidah; Wong Azman, Amelia

    2017-11-01

    Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algorithm is used to determine evict page when the cache is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may discard important pages just before use. Furthermore, using conventional algorithm cannot be well optimized since it requires some decision to intelligently evict a page before replacement. Hence, most researchers propose an integration among intelligent classifiers and replacement algorithm to improves replacement algorithms performance. This research proposes using automated wrapper feature selection methods to choose the best subset of features that are relevant and influence classifiers prediction accuracy. The result present that using wrapper feature selection methods namely: Best First (BFS), Incremental Wrapper subset selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of features and have a good impact on reducing computation time. Using PSO enhance NB classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04% over using J48 classifier with all features, using IWSS-embedded NB and using BFS respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation time of J48 by 2.998.

  15. Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

    Science.gov (United States)

    Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  16. An Appraisal Model Based on a Synthetic Feature Selection Approach for Students’ Academic Achievement

    Directory of Open Access Journals (Sweden)

    Ching-Hsue Cheng

    2017-11-01

    Full Text Available Obtaining necessary information (and even extracting hidden messages from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years because it can be used to find historical patterns and employ machine learning to aid in decision-making. When we find unexpected rules or patterns from the data, they are likely to be of high value. This paper proposes a synthetic feature selection approach (SFSA, which is combined with a support vector machine (SVM to extract patterns and find the key features that influence students’ academic achievement. For verifying the proposed model, two databases, namely, “Student Profile” and “Tutorship Record”, were collected from an elementary school in Taiwan, and were concatenated into an integrated dataset based on students’ names as a research dataset. The results indicate the following: (1 the accuracy of the proposed feature selection approach is better than that of the Minimum-Redundancy-Maximum-Relevance (mRMR approach; (2 the proposed model is better than the listing methods when the six least influential features have been deleted; and (3 the proposed model can enhance the accuracy and facilitate the interpretation of the pattern from a hybrid-type dataset of students’ academic achievement.

  17. Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

    Science.gov (United States)

    Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

    2015-01-01

    There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

  18. A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used...

  19. The effect of destination linked feature selection in real-time network intrusion detection

    CSIR Research Space (South Africa)

    Mzila, P

    2013-07-01

    Full Text Available techniques in the network intrusion detection system (NIDS) is the feature selection technique. The ability of NIDS to accurately identify intrusion from the network traffic relies heavily on feature selection, which describes the pattern of the network...

  20. GAIN RATIO BASED FEATURE SELECTION METHOD FOR PRIVACY PRESERVATION

    Directory of Open Access Journals (Sweden)

    R. Praveena Priyadarsini

    2011-04-01

    Full Text Available Privacy-preservation is a step in data mining that tries to safeguard sensitive information from unsanctioned disclosure and hence protecting individual data records and their privacy. There are various privacy preservation techniques like k-anonymity, l-diversity and t-closeness and data perturbation. In this paper k-anonymity privacy protection technique is applied to high dimensional datasets like adult and census. since, both the data sets are high dimensional, feature subset selection method like Gain Ratio is applied and the attributes of the datasets are ranked and low ranking attributes are filtered to form new reduced data subsets. K-anonymization privacy preservation technique is then applied on reduced datasets. The accuracy of the privacy preserved reduced datasets and the original datasets are compared for their accuracy on the two functionalities of data mining namely classification and clustering using naïve Bayesian and k-means algorithm respectively. Experimental results show that classification and clustering accuracy are comparatively the same for reduced k-anonym zed datasets and the original data sets.

  1. Soft computing based feature selection for environmental sound classification

    NARCIS (Netherlands)

    Shakoor, A.; May, T.M.; Van Schijndel, N.H.

    2010-01-01

    Environmental sound classification has a wide range of applications,like hearing aids, mobile communication devices, portable media players, and auditory protection devices. Sound classification systemstypically extract features from the input sound. Using too many features increases complexity

  2. Efficient Generation and Selection of Combined Features for Improved Classification

    KAUST Repository

    Shono, Ahmad N.

    2014-01-01

    This study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original

  3. The application of feature selection to the development of Gaussian process models for percutaneous absorption.

    Science.gov (United States)

    Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

    2010-06-01

    The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it

  4. Optimized maintenance concept of safety relevant valves related to ageing management features in nuclear power plants

    International Nuclear Information System (INIS)

    Koring, R.

    2007-01-01

    This paper presents the existing concept in E.ON Kernkraft and its sound application to ageing management issues by focussing on group 2 components such as safety relevant valves. It is demonstrated how the maintenance concept of safety relevant valves is supported by a valve diagnostic system accompanied by an applied procedure to assess the measured results with respect to the required functionality and ageing phenomena. Furthermore this concept has been developed to optimize the existing preventive maintenance of the safety relevant valves by implementing condition oriented aspects derived from the diagnostic results. The main issue of this maintenance concept is to demonstrate the high level of the secured function, reliability and performance of the safety relevant valves within an integrated ageing management. Additionally it offers improvements of all preventive maintenance issues as maintenance periods and the component related volume, spare parts management and costs. (author)

  5. Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

    International Nuclear Information System (INIS)

    Wang Xiaojia; Mao Qirong; Zhan Yongzhao

    2008-01-01

    There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction

  6. License Application Design Selection Feature Report: Waste Package Self Shielding Design Feature 13

    International Nuclear Information System (INIS)

    Tang, J.S.

    2000-01-01

    In the Viability Assessment (VA) reference design, handling of waste packages (WPs) in the emplacement drifts is performed remotely, and human access to the drifts is precluded when WPs are present. This report will investigate the feasibility of using a self-shielded WP design to reduce the radiation levels in the emplacement drifts to a point that, when coupled with ventilation, will create an acceptable environment for human access. This provides the benefit of allowing human entry to emplacement drifts to perform maintenance on ground support and instrumentation, and carry out performance confirmation activities. More direct human control of WP handling and emplacement operations would also be possible. However, these potential benefits must be weighed against the cost of implementation, and potential impacts on pre- and post-closure performance of the repository and WPs. The first section of this report will provide background information on previous investigations of the self-shielded WP design feature, summarize the objective and scope of this document, and provide quality assurance and software information. A shielding performance and cost study that includes several candidate shield materials will then be performed in the subsequent section to allow selection of two self-shielded WP design options for further evaluation. Finally, the remaining sections will evaluate the impacts of the two WP self-shielding options on the repository design, operations, safety, cost, and long-term performance of the WPs with respect to the VA reference design

  7. AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

    Science.gov (United States)

    Sun, Lei; Wang, Jun; Wei, Jinmao

    2017-03-14

    The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

  8. Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma.

    Science.gov (United States)

    Thomaz, Ricardo de Lima; Carneiro, Pedro Cunha; Bonin, João Eliton; Macedo, Túlio Augusto Alves; Patrocinio, Ana Claudia; Soares, Alcimar Barbosa

    2018-05-01

    Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.

  9. Feature Selection and Kernel Learning for Local Learning-Based Clustering.

    Science.gov (United States)

    Zeng, Hong; Cheung, Yiu-ming

    2011-08-01

    The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

  10. On Feature Relevance in Image-Based Prediction Models: An Empirical Study

    DEFF Research Database (Denmark)

    Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

    2013-01-01

    Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the co...

  11. Relevant feature set estimation with a knock-out strategy and random forests

    DEFF Research Database (Denmark)

    Ganz, Melanie; Greve, Douglas N; Fischl, Bruce

    2015-01-01

    unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a "knock-out" strategy and the Random Forest...

  12. The gender pay gap in the Australian private sector: Is selection relevant across the wage distribution?

    OpenAIRE

    Chzhen, Yekaterina; Mumford, Karen A.; Nicodemo, Catia

    2012-01-01

    We use quantile regression and counterfactual decomposition methods to explore gender gaps across the earning distribution for full-time employees in the Australian private sector. Significant evidence of a self selection effect for women into full-time employment (or of components of self selection related to observable or unobservable characteristics) is, interestingly, not found to be relevant in the Australian context. Substantial gender earnings gaps (and glass ceilings) are established,...

  13. Adaptive attunement of selective covert attention to evolutionary-relevant emotional visual scenes

    OpenAIRE

    Fernández-Martín, Andrés (UNIR); Gutiérrez-García, Aida; Capafons, Juan; Calvo, Manuel G

    2017-01-01

    We investigated selective attention to emotional scenes in peripheral vision, as a function of adaptive relevance of scene affective content for male and female observers. Pairs of emotional neutral images appeared peripherally with perceptual stimulus differences controlled while viewers were fixating on a different stimulus in central vision. Early selective orienting was assessed by the probability of directing the first fixation towards either scene, and the time until first fixation. Emo...

  14. On the relevance of sharp gamma-ray features for indirect dark matter searches

    International Nuclear Information System (INIS)

    Bringmann, Torsten; Calore, Francesca; Weniger, Christoph

    2011-06-01

    Gamma rays from the annihilation of dark matter particles in the Galactic halo provide a particularly promising means of indirectly detecting dark matter. Here, we demonstrate that pronounced spectral features near the kinematic cutoff at the dark matter particles' mass, which is a generic prediction for most models, can significantly improve the sensitivity of gamma-ray telescopes to dark matter signals. We derive projected limits on such features (including the traditionally looked-for line signals) and show that these can be very efficient in constraining the nature of dark matter. (orig.)

  15. Clustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes

    KAUST Repository

    Abusamra, Heba

    2016-07-20

    The native nature of high dimension low sample size of gene expression data make the classification task more challenging. Therefore, feature (gene) selection become an apparent need. Selecting a meaningful and relevant genes for classifier not only decrease the computational time and cost, but also improve the classification performance. Among different approaches of feature selection methods, however most of them suffer from several problems such as lack of robustness, validation issues etc. Here, we present a new feature selection technique that takes advantage of clustering both samples and genes. Materials and methods We used leukemia gene expression dataset [1]. The effectiveness of the selected features were evaluated by four different classification methods; support vector machines, k-nearest neighbor, random forest, and linear discriminate analysis. The method evaluate the importance and relevance of each gene cluster by summing the expression level for each gene belongs to this cluster. The gene cluster consider important, if it satisfies conditions depend on thresholds and percentage otherwise eliminated. Results Initial analysis identified 7120 differentially expressed genes of leukemia (Fig. 15a), after applying our feature selection methodology we end up with specific 1117 genes discriminating two classes of leukemia (Fig. 15b). Further applying the same method with more stringent higher positive and lower negative threshold condition, number reduced to 58 genes have be tested to evaluate the effectiveness of the method (Fig. 15c). The results of the four classification methods are summarized in Table 11. Conclusions The feature selection method gave good results with minimum classification error. Our heat-map result shows distinct pattern of refines genes discriminating between two classes of leukemia.

  16. Integrative approaches to the prediction of protein functions based on the feature selection

    Directory of Open Access Journals (Sweden)

    Lee Hyunju

    2009-12-01

    Full Text Available Abstract Background Protein function prediction has been one of the most important issues in functional genomics. With the current availability of various genomic data sets, many researchers have attempted to develop integration models that combine all available genomic data for protein function prediction. These efforts have resulted in the improvement of prediction quality and the extension of prediction coverage. However, it has also been observed that integrating more data sources does not always increase the prediction quality. Therefore, selecting data sources that highly contribute to the protein function prediction has become an important issue. Results We present systematic feature selection methods that assess the contribution of genome-wide data sets to predict protein functions and then investigate the relationship between genomic data sources and protein functions. In this study, we use ten different genomic data sources in Mus musculus, including: protein-domains, protein-protein interactions, gene expressions, phenotype ontology, phylogenetic profiles and disease data sources to predict protein functions that are labelled with Gene Ontology (GO terms. We then apply two approaches to feature selection: exhaustive search feature selection using a kernel based logistic regression (KLR, and a kernel based L1-norm regularized logistic regression (KL1LR. In the first approach, we exhaustively measure the contribution of each data set for each function based on its prediction quality. In the second approach, we use the estimated coefficients of features as measures of contribution of data sources. Our results show that the proposed methods improve the prediction quality compared to the full integration of all data sources and other filter-based feature selection methods. We also show that contributing data sources can differ depending on the protein function. Furthermore, we observe that highly contributing data sets can be similar among

  17. Memory for Physical Features of Discourse as a Function of Their Relevance.

    Science.gov (United States)

    Fisher, Ronald P.; Cuervo, Asela

    Memory for sex of the speaker and language of presentation of a spoken message was high and reliably better when the features were instrumental for comprehending the message than when they were not. This suggests that the physical characteristics of an event may be deeply or elaborately encoded when they are meaningful in light of the task…

  18. Sequence-based classification using discriminatory motif feature selection.

    Directory of Open Access Journals (Sweden)

    Hao Xiong

    Full Text Available Most existing methods for sequence-based classification use exhaustive feature generation, employing, for example, all k-mer patterns. The motivation behind such (enumerative approaches is to minimize the potential for overlooking important features. However, there are shortcomings to this strategy. First, practical constraints limit the scope of exhaustive feature generation to patterns of length ≤ k, such that potentially important, longer (> k predictors are not considered. Second, features so generated exhibit strong dependencies, which can complicate understanding of derived classification rules. Third, and most importantly, numerous irrelevant features are created. These concerns can compromise prediction and interpretation. While remedies have been proposed, they tend to be problem-specific and not broadly applicable. Here, we develop a generally applicable methodology, and an attendant software pipeline, that is predicated on discriminatory motif finding. In addition to the traditional training and validation partitions, our framework entails a third level of data partitioning, a discovery partition. A discriminatory motif finder is used on sequences and associated class labels in the discovery partition to yield a (small set of features. These features are then used as inputs to a classifier in the training partition. Finally, performance assessment occurs on the validation partition. Important attributes of our approach are its modularity (any discriminatory motif finder and any classifier can be deployed and its universality (all data, including sequences that are unaligned and/or of unequal length, can be accommodated. We illustrate our approach on two nucleosome occupancy datasets and a protein solubility dataset, previously analyzed using enumerative feature generation. Our method achieves excellent performance results, with and without optimization of classifier tuning parameters. A Python pipeline implementing the approach is

  19. On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    Asriyanti Indah Pratiwi

    2018-01-01

    Full Text Available Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.

  20. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

    2017-01-01

    With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

  1. Selecting protein families for environmental features based on manifold regularization.

    Science.gov (United States)

    Jiang, Xingpeng; Xu, Weiwei; Park, E K; Li, Guangrong

    2014-06-01

    Recently, statistics and machine learning have been developed to identify functional or taxonomic features of environmental features or physiological status. Important proteins (or other functional and taxonomic entities) to environmental features can be potentially used as biosensors. A major challenge is how the distribution of protein and gene functions embodies the adaption of microbial communities across environments and host habitats. In this paper, we propose a novel regularization method for linear regression to adapt the challenge. The approach is inspired by local linear embedding (LLE) and we call it a manifold-constrained regularization for linear regression (McRe). The novel regularization procedure also has potential to be used in solving other linear systems. We demonstrate the efficiency and the performance of the approach in both simulation and real data.

  2. Knowledge-based driver assistance systems traffic situation description and situation feature relevance

    CERN Document Server

    Huelsen, Michael

    2014-01-01

    The comprehension of a traffic situation plays a major role in driving a vehicle. Interpretable information forms a basis for future projection, decision making and action performing, such as navigating, maneuvering and driving control. Michael Huelsen provides an ontology-based generic traffic situation description capable of supplying various advanced driver assistance systems with relevant information about the current traffic situation of a vehicle and its environment. These systems are enabled to perform reasonable actions and approach visionary goals such as injury and accident free driv

  3. Age Differences in Selective Memory of Goal-Relevant Stimuli Under Threat.

    Science.gov (United States)

    Durbin, Kelly A; Clewett, David; Huang, Ringo; Mather, Mara

    2018-02-01

    When faced with threat, people often selectively focus on and remember the most pertinent information while simultaneously ignoring any irrelevant information. Filtering distractors under arousal requires inhibitory mechanisms, which take time to recruit and often decline in older age. Despite the adaptive nature of this ability, relatively little research has examined how both threat and time spent preparing these inhibitory mechanisms affect selective memory for goal-relevant information across the life span. In this study, 32 younger and 31 older adults were asked to encode task-relevant scenes, while ignoring transparent task-irrelevant objects superimposed onto them. Threat levels were increased on some trials by threatening participants with monetary deductions if they later forgot scenes that followed threat cues. We also varied the time between threat induction and a to-be-encoded scene (i.e., 2 s, 4 s, 6 s) to determine whether both threat and timing effects on memory selectivity differ by age. We found that age differences in memory selectivity only emerged after participants spent a long time (i.e., 6 s) preparing for selective encoding. Critically, this time-dependent age difference occurred under threatening, but not neutral, conditions. Under threat, longer preparation time led to enhanced memory for task-relevant scenes and greater memory suppression of task-irrelevant objects in younger adults. In contrast, increased preparation time after threat induction had no effect on older adults' scene memory and actually worsened memory suppression of task-irrelevant objects. These findings suggest that increased time to prepare top-down encoding processes benefits younger, but not older, adults' selective memory for goal-relevant information under threat. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  4. A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics

    Directory of Open Access Journals (Sweden)

    Bharat Singh

    2014-11-01

    Full Text Available A high-dimensional feature selection having a very large number of features with an optimal feature subset is an NP-complete problem. Because conventional optimization techniques are unable to tackle large-scale feature selection problems, meta-heuristic algorithms are widely used. In this paper, we propose a particle swarm optimization technique while utilizing regression techniques for feature selection. We then use the selected features to classify the data. Classification accuracy is used as a criterion to evaluate classifier performance, and classification is accomplished through the use of k-nearest neighbour (KNN and Bayesian techniques. Various high dimensional data sets are used to evaluate the usefulness of the proposed approach. Results show that our approach gives better results when compared with other conventional feature selection algorithms.

  5. Feature selection for domain knowledge representation through multitask learning

    CSIR Research Space (South Africa)

    Rosman, Benjamin S

    2014-10-01

    Full Text Available represent stimuli of interest, and rich feature sets which increase the dimensionality of the space and thus the difficulty of the learning problem. We focus on a multitask reinforcement learning setting, where the agent is learning domain knowledge...

  6. Automatic selective feature retention in patient specific elastic surface registration

    CSIR Research Space (South Africa)

    Jansen van Rensburg, GJ

    2011-01-01

    Full Text Available The accuracy with which a recent elastic surface registration algorithm deforms the complex geometry of a skull is examined. This algorithm is then coupled to a line based algorithm as is frequently used in patient specific feature registration...

  7. Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection.

    Science.gov (United States)

    Kipli, Kuryati; Kouzani, Abbas Z

    2015-07-01

    Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression. A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation. The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score. DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

  8. UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

    Directory of Open Access Journals (Sweden)

    A. Kianisarkaleh

    2015-12-01

    Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

  9. Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Shengyun Liang

    2015-11-01

    Full Text Available The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers participated in functional movement tests, including walking and sit-to-stand (STS. A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN, pseudo nearest neighbor (PNN, local mean pseudo nearest neighbor (LMPNN classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

  10. Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

    OpenAIRE

    Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

    2015-01-01

    Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

  11. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    Science.gov (United States)

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  12. Our Selections and Decisions: Inherent Features of the Nervous System?

    Science.gov (United States)

    Rösler, Frank

    The chapter summarizes findings on the neuronal bases of decisionmaking. Taking the phenomenon of selection it will be explained that systems built only from excitatory and inhibitory neuron (populations) have the emergent property of selecting between different alternatives. These considerations suggest that there exists a hierarchical architecture with central selection switches. However, in such a system, functions of selection and decision-making are not localized, but rather emerge from an interaction of several participating networks. These are, on the one hand, networks that process specific input and output representations and, on the other hand, networks that regulate the relative activation/inhibition of the specific input and output networks. These ideas are supported by recent empirical evidence. Moreover, other studies show that rather complex psychological variables, like subjective probability estimates, expected gains and losses, prediction errors, etc., do have biological correlates, i.e., they can be localized in time and space as activation states of neural networks and single cells. These findings suggest that selections and decisions are consequences of an architecture which, seen from a biological perspective, is fully deterministic. However, a transposition of such nomothetic functional principles into the idiographic domain, i.e., using them as elements for comprehensive 'mechanistic' explanations of individual decisions, seems not to be possible because of principle limitations. Therefore, individual decisions will remain predictable by means of probabilistic models alone.

  13. Selecting Features Of A Web Platform To Enhance Course Delivery

    OpenAIRE

    Karen A. Berger; Martin T. Topol

    2011-01-01

    This paper reviews key features of popular Web platforms used for course delivery. Institutions of higher education have rushed to adopt these platforms for several reasons. From the point of view of the educator, the most important reason is to enhance the classroom experience (real or virtual). Classroom experiences can benefit from a continuous stream of discourse made possible by the communications tools available in the web platforms designed for educational application. In addition, web...

  14. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  15. Forgotten Features of Head Zones and Their Relation to Diagnostically Relevant Acupuncture Points

    Directory of Open Access Journals (Sweden)

    Florian Beissner

    2011-01-01

    Full Text Available In the 1890s Sir Henry Head discovered certain areas of the skin that develop tenderness (allodynia in the course of visceral disease. These areas were later termed “Head zones”. In addition, he also emphasized the existence of specific points within these zones, that he called “maximum points”, a finding that seems to be almost forgotten today. We hypothesized that two important groups of acupuncture points, the diagnostically relevant Mu and Shu points, spatially and functionally coincide with these maximum points to a large extent. A comparison of Head's papers with the Huang Di Neijing (Yellow Thearch's Inner Classic and the Zhen Jiu Jia Yi Jing (Systematic Classic of Acupuncture and Moxibustion, two of the oldest still extant Chinese sources on acupuncture, revealed astonishing parallels between the two concepts regarding both point locations and functional aspects. These findings suggest that the Chinese discovery of viscerocutaneous reflexes preceded the discovery in the West by more than 2000 years. Furthermore, the fact that Chinese medicine uses Mu and Shu points not only diagnostically but also therapeutically may give us new insights into the underlying mechanisms of acupuncture.

  16. Relevant Features of a Triethylene Glycol Dimethyl Ether-Based Electrolyte for Application in Lithium Battery.

    Science.gov (United States)

    Carbone, Lorenzo; Di Lecce, Daniele; Gobet, Mallory; Munoz, Stephen; Devany, Matthew; Greenbaum, Steve; Hassoun, Jusef

    2017-05-24

    Triethylene glycol dimethyl ether (TREGDME) dissolving lithium trifluoromethanesulfonate (LiCF 3 SO 3 ) is studied as a suitable electrolyte medium for lithium battery. Thermal and rheological characteristics, transport properties of the dissolved species, and the electrochemical behavior in lithium cell represent the most relevant investigated properties of the new electrolyte. The self-diffusion coefficients, the lithium transference numbers, the ionic conductivity, and the ion association degree of the solution are determined by pulse field gradient nuclear magnetic resonance and electrochemical impedance spectroscopy. The study sheds light on the determinant role of the lithium nitrate (LiNO 3 ) addition for allowing cell operation by improving the electrode/electrolyte interfaces and widening the voltage stability window. Accordingly, an electrochemical activation procedure of the Li/LiFePO 4 cell using the upgraded electrolyte leads to the formation of stable interfaces at the electrodes surface as clearly evidenced by cyclic voltammetry, impedance spectroscopy, and ex situ scanning electron microscopy. Therefore, the lithium battery employing the TREGDME-LiCF 3 SO 3 -LiNO 3 solution shows a stable galvanostatic cycling, a high efficiency, and a notable rate capability upon the electrochemical conditions adopted herein.

  17. EFL/ESL Textbook Selection in Korea and East Asia - Relevant Issues and Literature Review

    Science.gov (United States)

    Meurant, Robert C.

    EFL/ESL departments periodically face the problem of textbook selection. Cogent issues are that non-native speakers will use L2 English mainly to communicate with other non-native English speakers, so an American accent is becoming less important. L2 English will mainly be used in computer-mediated communication, hence the importance of L2 Digital Literacy. The convergence of Information Communication Technologies is radically impacting Second Language Acquisition, which is integrating web-hosted Assessment and Learning Management Systems. EFL/ESL textbooks need to be compatible with blended learning, prepare students for a globalized world, and foster autonomous learning. I summarize five papers on EFL/ESL textbook evaluation and selection, and include relevant material for adaptation. Textbooks are major sources of contact with the target language, so selection is an important decision. Educators need to be systematic and objective in their approach, adopting a selection process that is open, transparent, accountable, participatory, informed and rigorous.

  18. Maximum relevance, minimum redundancy band selection based on neighborhood rough set for hyperspectral data classification

    International Nuclear Information System (INIS)

    Liu, Yao; Chen, Yuehua; Tan, Kezhu; Xie, Hong; Wang, Liguo; Xie, Wu; Yan, Xiaozhen; Xu, Zhen

    2016-01-01

    Band selection is considered to be an important processing step in handling hyperspectral data. In this work, we selected informative bands according to the maximal relevance minimal redundancy (MRMR) criterion based on neighborhood mutual information. Two measures MRMR difference and MRMR quotient were defined and a forward greedy search for band selection was constructed. The performance of the proposed algorithm, along with a comparison with other methods (neighborhood dependency measure based algorithm, genetic algorithm and uninformative variable elimination algorithm), was studied using the classification accuracy of extreme learning machine (ELM) and random forests (RF) classifiers on soybeans’ hyperspectral datasets. The results show that the proposed MRMR algorithm leads to promising improvement in band selection and classification accuracy. (paper)

  19. Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

    Science.gov (United States)

    Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

    Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

  20. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  1. Automated selection of relevant information for notification of incident cancer cases within a multisource cancer registry.

    Science.gov (United States)

    Jouhet, V; Defossez, G; Ingrand, P

    2013-01-01

    The aim of this study was to develop and evaluate a selection algorithm of relevant records for the notification of incident cases of cancer on the basis of the individual data available in a multi-source information system. This work was conducted on data for the year 2008 in the general cancer registry of Poitou-Charentes region (France). The selection algorithm hierarchizes information according to its level of relevance for tumoral topography and tumoral morphology independently. The selected data are combined to form composite records. These records are then grouped in respect with the notification rules of the International Agency for Research on Cancer for multiple primary cancers. The evaluation, based on recall, precision and F-measure confronted cases validated manually by the registry's physicians with tumours notified with and without records selection. The analysis involved 12,346 tumours validated among 11,971 individuals. The data used were hospital discharge data (104,474 records), pathology data (21,851 records), healthcare insurance data (7508 records) and cancer care centre's data (686 records). The selection algorithm permitted performances improvement for notification of tumour topography (F-measure 0.926 with vs. 0.857 without selection) and tumour morphology (F-measure 0.805 with vs. 0.750 without selection). These results show that selection of information according to its origin is efficient in reducing noise generated by imprecise coding. Further research is needed for solving the semantic problems relating to the integration of heterogeneous data and the use of non-structured information.

  2. Using ANFIS for selection of more relevant parameters to predict dew point temperature

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Shamshirband, Shahaboddin; Petković, Dalibor; Yee, Por Lip; Mansor, Zulkefli

    2016-01-01

    Highlights: • ANFIS is used to select the most relevant variables for dew point temperature prediction. • Two cities from the central and south central parts of Iran are selected as case studies. • Influence of 5 parameters on dew point temperature is evaluated. • Appropriate selection of input variables has a notable effect on prediction. • Considering the most relevant combination of 2 parameters would be more suitable. - Abstract: In this research work, for the first time, the adaptive neuro fuzzy inference system (ANFIS) is employed to propose an approach for identifying the most significant parameters for prediction of daily dew point temperature (T_d_e_w). The ANFIS process for variable selection is implemented, which includes a number of ways to recognize the parameters offering favorable predictions. According to the physical factors influencing the dew formation, 8 variables of daily minimum, maximum and average air temperatures (T_m_i_n, T_m_a_x and T_a_v_g), relative humidity (R_h), atmospheric pressure (P), water vapor pressure (V_P), sunshine hour (n) and horizontal global solar radiation (H) are considered to investigate their effects on T_d_e_w. The used data include 7 years daily measured data of two Iranian cities located in the central and south central parts of the country. The results indicate that despite climate difference between the considered case studies, for both stations, V_P is the most influential variable while R_h is the least relevant element. Furthermore, the combination of T_m_i_n and V_P is recognized as the most influential set to predict T_d_e_w. The conducted examinations show that there is a remarkable difference between the errors achieved for most and less relevant input parameters, which highlights the importance of appropriate selection of input parameters. The use of more than two inputs may not be advisable and appropriate; thus, considering the most relevant combination of 2 parameters would be more suitable

  3. Features of mechanical snubbers and the method of selection

    Energy Technology Data Exchange (ETDEWEB)

    Sunakoda, K [Sanwa Tekki Corp., Utsunomiya (Japan). Utsunomiya Works

    1978-11-01

    In the oil snubbers used in the high radiation environment of nuclear power stations, gas generation from oil and the deterioration of rubber material for sealing occur due to radiation damage, therefore periodical inspection and replacement are required during operation. The mechanical snubbers developed as aseismatic supporters in place of oil snubbers have entered the stage of practical use, and are made by two companies in USA and a company in Japan. Their features as compared with oil snubbers are presented.ces are explained.

  4. Mutual information based feature selection for medical image retrieval

    Science.gov (United States)

    Zhi, Lijia; Zhang, Shaomin; Li, Yan

    2018-04-01

    In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

  5. The relevance of clinical and radiographic features of jaw lesions: A prospective study

    Directory of Open Access Journals (Sweden)

    Juliane Piragine ARAUJO

    Full Text Available Abstract The study was carried out in a Brazilian population and the aim was to describe the prevalence and the clinic-radiographical features of jaw lesions. In addition, a comparison between the main diagnosis hypothesis and final diagnosis was accessed. A prospective study which evaluated all patients with jaw lesions diagnosed in an Oral Diagnosis Center, between August 2013 and October 2014. A total of 450 patients were observed for the first time, and 130 had some type of jaw lesion. The mean age of the patients was 35.2 years ± 17.86. Among these, 71 were women (54.62% and 87 were Caucasian (66.92%. The mandible was affected more frequently (71.43% than the maxilla (28.57%. Swelling and pain were the most frequent clinical signs and symptoms and were observed in 60 (42.85% and 38 (27.14% cases, respectively. The panoramic x-ray was the main radiographic exam utilized (88.57%. Radiolucent lesions accounted for 89 cases (63.57% and the unilocular form was present in 114 cases (81.43%. A total of 93 cases had histopathological analyses and the periapical cyst was the most frequent lesion. In the other 47 lesions, the diagnosis was conducted by clinical and radiographic management. Bone lesions were frequent, being noted on first visit in approximately 30% of patients; in 1/3 of the cases, the diagnoses were completed with a combination of clinical and radiographic exams.

  6. Molecular Features Underlying Selectivity in Chicken Bitter Taste Receptors

    Directory of Open Access Journals (Sweden)

    Antonella Di Pizio

    2018-01-01

    Full Text Available Chickens sense the bitter taste of structurally different molecules with merely three bitter taste receptors (Gallus gallus taste 2 receptors, ggTas2rs, representing a minimal case of bitter perception. Some bitter compounds like quinine, diphenidol and chlorpheniramine, activate all three ggTas2rs, while others selectively activate one or two of the receptors. We focus on bitter compounds with different selectivity profiles toward the three receptors, to shed light on the molecular recognition complexity in bitter taste. Using homology modeling and induced-fit docking simulations, we investigated the binding modes of ggTas2r agonists. Interestingly, promiscuous compounds are predicted to establish polar interactions with position 6.51 and hydrophobic interactions with positions 3.32 and 5.42 in all ggTas2rs; whereas certain residues are responsible for receptor selectivity. Lys3.29 and Asn3.36 are suggested as ggTas2r1-specificity-conferring residues; Gln6.55 as ggTas2r2-specificity-conferring residue; Ser5.38 and Gln7.42 as ggTas2r7-specificity conferring residues. The selectivity profile of quinine analogs, quinidine, epiquinidine and ethylhydrocupreine, was then characterized by combining calcium-imaging experiments and in silico approaches. ggTas2r models were used to virtually screen BitterDB compounds. ~50% of compounds known to be bitter to human are likely to be bitter to chicken, with 25, 20, 37% predicted to be ggTas2r1, ggTas2r2, ggTas2r7 agonists, respectively. Predicted ggTas2rs agonists can be tested with in vitro and in vivo experiments, contributing to our understanding of bitter taste in chicken and, consequently, to the improvement of chicken feed.

  7. Secondary myeloid neoplasms: bone marrow cytogenetic and histological features may be relevant to prognosis

    Directory of Open Access Journals (Sweden)

    Roberta Sandra da Silva Tanizawa

    Full Text Available Abstract Background: Secondary myeloid neoplasms comprise a group of diseases arising after chemotherapy, radiation, immunosuppressive therapy or from aplastic anemia. Few studies have addressed prognostic factors in these neoplasms. Method: Forty-two patients diagnosed from 1987 to 2008 with secondary myeloid neoplasms were retrospectively evaluated concerning clinical, biochemical, peripheral blood, bone marrow aspirate, biopsy, and immunohistochemistry and cytogenetic features at diagnosis as prognostic factors. The International Prognostic Scoring System was applied. Statistical analysis employed the Kaplan–Meier method, log-rank and Fisher's exact test. Results: Twenty-three patients (54.8% were male and the median age was 53.5 years (range: 4–88 years at diagnosis of secondary myeloid neoplasms. Previous diseases included hematologic malignancies, solid tumors, aplastic anemia, autoimmune diseases and conditions requiring solid organ transplantations. One third of patients (33% were submitted to chemotherapy alone, 2% to radiotherapy, 26% to both modalities and 28% to immunosuppressive agents. Five patients (11.9% had undergone autologous hematopoietic stem cell transplantation. The median latency between the primary disease and secondary myeloid neoplasms was 85 months (range: 23–221 months. Eight patients were submitted to allogeneic hematopoietic stem cell transplantation to treat secondary myeloid neoplasms. Important changes in bone marrow were detected mainly by biopsy, immunohistochemistry and cytogenetics. The presence of clusters of CD117+ cells and p53+ cells were associated with low survival. p53 was associated to a higher risk according to the International Prognostic Scoring System. High prevalence of clonal abnormalities (84.3% and thrombocytopenia (78.6% were independent factors for poor survival. Conclusion: This study demonstrated that cytogenetics, bone marrow biopsy and immunohistochemistry are very important

  8. Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

    Science.gov (United States)

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

  9. Quantitative analysis and relevant features of the scientific literature related to SAXS and SANS

    International Nuclear Information System (INIS)

    Craievich, Aldo F; Fischer, Hannes

    2010-01-01

    We present and discuss here numerical information derived from a systematic searching of scientific papers related to SAXS and SANS published in indexed journals - from 1945 until nowadays - recorded by the Web of Science Data Bank (WoS). We have detected interesting features regarding the time dependence of the number of papers/year, N(t), indicating the existence of three well-defined periods of historical evolution with rather well-defined boundaries. All three periods exhibit a positive and approximately linear variation of N(t) but, at the two transitions between periods, the rate of growth exhibits clear and strong increases. Differences of the historical evolutions in the numbers of papers/year related to SAXS and to SANS were established. The different behaviours regarding the numbers of papers/year related to SAXS and to SANS and the existence of three different and well defined periods for N(t) can be qualitatively understood as a consequence of the progressive and increasing availability along the last three decades of very brilliant synchrotrons, last generation commercial X-ray sources, new neutron facilities, powerful computers and novel theoretical approaches for SAS data analysis. The rates of growth in the number of papers/year published by authors from a set of different countries are approximately constant along the last two decades. For other countries we have detected a slowing down effect in the number of papers/year while a clear acceleration could be noticed for the production of SAS papers by authors from several emerging countries. These opposite trends compensate in such a way that the number of SAS (SAXS+SAXS) articles published per year all around the world maintained a vigorous linear growth - during more than 20 years - at a constant rate of 60 papers/year, without any indication of eventual saturation. The observed distribution of articles among different journals indicates that a very high fraction of the volume of SAS research is

  10. A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

    Science.gov (United States)

    Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

    2018-02-01

    Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

  11. Feature Selection Methods for Zero-Shot Learning of Neural Activity

    Directory of Open Access Journals (Sweden)

    Carlos A. Caceres

    2017-06-01

    Full Text Available Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

  12. Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach

    Directory of Open Access Journals (Sweden)

    Daniel Peralta

    2015-01-01

    Full Text Available Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.

  13. DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

    Science.gov (United States)

    Ma, Xin; Guo, Jing; Sun, Xiao

    2016-01-01

    DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

  14. Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

    NARCIS (Netherlands)

    Aalaei, Shokoufeh; Shahraki, Hadi; Rowhanimanesh, Alireza; Eslami, Saeid

    2016-01-01

    This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. To

  15. Feature selection is the ReliefF for multiple instance learning

    NARCIS (Netherlands)

    Zafra, A.; Pechenizkiy, M.; Ventura, S.

    2010-01-01

    Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In

  16. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

    Science.gov (United States)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized

  17. Effect of feature-selective attention on neuronal responses in macaque area MT

    Science.gov (United States)

    Chen, X.; Hoffmann, K.-P.; Albright, T. D.

    2012-01-01

    Attention influences visual processing in striate and extrastriate cortex, which has been extensively studied for spatial-, object-, and feature-based attention. Most studies exploring neural signatures of feature-based attention have trained animals to attend to an object identified by a certain feature and ignore objects/displays identified by a different feature. Little is known about the effects of feature-selective attention, where subjects attend to one stimulus feature domain (e.g., color) of an object while features from different domains (e.g., direction of motion) of the same object are ignored. To study this type of feature-selective attention in area MT in the middle temporal sulcus, we trained macaque monkeys to either attend to and report the direction of motion of a moving sine wave grating (a feature for which MT neurons display strong selectivity) or attend to and report its color (a feature for which MT neurons have very limited selectivity). We hypothesized that neurons would upregulate their firing rate during attend-direction conditions compared with attend-color conditions. We found that feature-selective attention significantly affected 22% of MT neurons. Contrary to our hypothesis, these neurons did not necessarily increase firing rate when animals attended to direction of motion but fell into one of two classes. In one class, attention to color increased the gain of stimulus-induced responses compared with attend-direction conditions. The other class displayed the opposite effects. Feature-selective activity modulations occurred earlier in neurons modulated by attention to color compared with neurons modulated by attention to motion direction. Thus feature-selective attention influences neuronal processing in macaque area MT but often exhibited a mismatch between the preferred stimulus dimension (direction of motion) and the preferred attention dimension (attention to color). PMID:22170961

  18. A PANEL REGRESSION ANALYSIS OF HUMAN CAPITAL RELEVANCE IN SELECTED SCANDINAVIAN AND SE EUROPEAN COUNTRIES

    Directory of Open Access Journals (Sweden)

    Filip Kokotovic

    2016-06-01

    Full Text Available The study of human capital relevance to economic growth is becoming increasingly important taking into account its relevance in many of the Sustainable Development Goals proposed by the UN. This paper conducted a panel regression analysis of selected SE European countries and Scandinavian countries using the Granger causality test and pooled panel regression. In order to test the relevance of human capital on economic growth, several human capital proxy variables were identified. Aside from the human capital proxy variables, other explanatory variables were selected using stepwise regression while the dependant variable was GDP. This paper concludes that there are significant structural differences in the economies of the two observed panels. Of the human capital proxy variables observed, for the panel of SE European countries only life expectancy was statistically significant and it had a negative impact on economic growth, while in the panel of Scandinavian countries total public expenditure on education had a statistically significant positive effect on economic growth. Based upon these results and existing studies, this paper concludes that human capital has a far more significant impact on economic growth in more developed economies.

  19. An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

    Directory of Open Access Journals (Sweden)

    Jing Bian

    2016-01-01

    Full Text Available In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 and k-nearest neighbor (KNN. The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

  20. A Vietnamese man with selective mutism: the relevance of multiple interacting 'cultures' in clinical psychiatry.

    Science.gov (United States)

    Hollifield, Michael; Geppert, Cynthia; Johnson, Yuam; Fryer, Carol

    2003-09-01

    Multiple cultural variables have effects on the psychobiology and behavioral manifestations of illness, as do patient and physician perceptions of illness. The interaction among these variables is at the heart of clinical psychiatry. This case of a Vietnamese man with selective mutism underscores the relevance of the 'cultures' of medicine, psychiatry, and war and trauma on the manifestations of illness and illness perceptions by patient and physician. The discussion focuses on how these cultures interact and play a crucial role in formulating diagnosis and treatment planning. Suggestions are given for shifts in medical education that will encourage relevant cultural paradigms to make their way into educational and clinical systems, which in turn should improve cultural competence in clinical psychiatry.

  1. Object learning improves feature extraction but does not improve feature selection.

    Directory of Open Access Journals (Sweden)

    Linus Holm

    Full Text Available A single glance at your crowded desk is enough to locate your favorite cup. But finding an unfamiliar object requires more effort. This superiority in recognition performance for learned objects has at least two possible sources. For familiar objects observers might: 1 select more informative image locations upon which to fixate their eyes, or 2 extract more information from a given eye fixation. To test these possibilities, we had observers localize fragmented objects embedded in dense displays of random contour fragments. Eight participants searched for objects in 600 images while their eye movements were recorded in three daily sessions. Performance improved as subjects trained with the objects: The number of fixations required to find an object decreased by 64% across the 3 sessions. An ideal observer model that included measures of fragment confusability was used to calculate the information available from a single fixation. Comparing human performance to the model suggested that across sessions information extraction at each eye fixation increased markedly, by an amount roughly equal to the extra information that would be extracted following a 100% increase in functional field of view. Selection of fixation locations, on the other hand, did not improve with practice.

  2. Adaptive feature selection using v-shaped binary particle swarm optimization.

    Science.gov (United States)

    Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

    2017-01-01

    Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

  3. Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm

    Science.gov (United States)

    Auat Cheein, Fernando A.; Carelli, Ricardo

    2011-01-01

    This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. This restriction also causes a decrement in the processing time of the SLAM. Several experiments with a mobile robot are shown in this work. The experiments concern the map reconstruction and a comparison between the different proposed techniques performance. The experiments were carried out at an outdoor environment composed by trees, although the results shown herein are not restricted to a special type of features. PMID:22346568

  4. Feature selection model based on clustering and ranking in pipeline for microarray data

    Directory of Open Access Journals (Sweden)

    Barnali Sahu

    2017-01-01

    Full Text Available Most of the available feature selection techniques in the literature are classifier bound. It means a group of features tied to the performance of a specific classifier as applied in wrapper and hybrid approach. Our objective in this study is to select a set of generic features not tied to any classifier based on the proposed framework. This framework uses attribute clustering and feature ranking techniques in pipeline in order to remove redundant features. On each uncovered cluster, signal-to-noise ratio, t-statistics and significance analysis of microarray are independently applied to select the top ranked features. Both filter and evolutionary wrapper approaches have been considered for feature selection and the data set with selected features are given to ensemble of predefined statistically different classifiers. The class labels of the test data are determined using majority voting technique. Moreover, with the aforesaid objectives, this paper focuses on obtaining a stable result out of various classification models. Further, a comparative analysis has been performed to study the classification accuracy and computational time of the current approach and evolutionary wrapper techniques. It gives a better insight into the features and further enhancing the classification accuracy with less computational time.

  5. Multi-task feature selection in microarray data by binary integer programming.

    Science.gov (United States)

    Lan, Liang; Vucetic, Slobodan

    2013-12-20

    A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

  6. Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

    Science.gov (United States)

    Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

    2015-01-01

    Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

  7. A consistency-based feature selection method allied with linear SVMs for HIV-1 protease cleavage site prediction.

    Directory of Open Access Journals (Sweden)

    Orkun Oztürk

    Full Text Available BACKGROUND: Predicting type-1 Human Immunodeficiency Virus (HIV-1 protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy. RESULTS: We propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs. We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations. CONCLUSION: Applying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper. SOFTWARE AVAILABILITY: The software is available at http

  8. An input feature selection method applied to fuzzy neural networks for signal esitmation

    International Nuclear Information System (INIS)

    Na, Man Gyun; Sim, Young Rok

    2001-01-01

    It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

  9. A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data

    KAUST Repository

    Abusamra, Heba

    2013-05-01

    Microarray technology has enriched the study of gene expression in such a way that scientists are now able to measure the expression levels of thousands of genes in a single experiment. Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification, interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This thesis aims on a comparative study of state-of-the-art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k- nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t- statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used for this study. Different experiments have been applied to compare the performance of the classification methods with and without performing feature selection. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in

  10. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

    Science.gov (United States)

    Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

    2017-12-26

    Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  11. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Xiaohui Lin

    2017-12-01

    Full Text Available Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  12. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    Science.gov (United States)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  13. Selection of individual features of a speech signal using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Kamil Kamiński

    2016-03-01

    Full Text Available The paper presents an automatic speaker’s recognition system, implemented in the Matlab environment, and demonstrates how to achieve and optimize various elements of the system. The main emphasis was put on features selection of a speech signal using a genetic algorithm which takes into account synergy of features. The results of optimization of selected elements of a classifier have been also shown, including the number of Gaussian distributions used to model each of the voices. In addition, for creating voice models, a universal voice model has been used.[b]Keywords[/b]: biometrics, automatic speaker recognition, genetic algorithms, feature selection

  14. Adaptive attunement of selective covert attention to evolutionary-relevant emotional visual scenes.

    Science.gov (United States)

    Fernández-Martín, Andrés; Gutiérrez-García, Aída; Capafons, Juan; Calvo, Manuel G

    2017-05-01

    We investigated selective attention to emotional scenes in peripheral vision, as a function of adaptive relevance of scene affective content for male and female observers. Pairs of emotional-neutral images appeared peripherally-with perceptual stimulus differences controlled-while viewers were fixating on a different stimulus in central vision. Early selective orienting was assessed by the probability of directing the first fixation towards either scene, and the time until first fixation. Emotional scenes selectively captured covert attention even when they were task-irrelevant, thus revealing involuntary, automatic processing. Sex of observers and specific emotional scene content (e.g., male-to-female-aggression, families and babies, etc.) interactively modulated covert attention, depending on adaptive priorities and goals for each sex, both for pleasant and unpleasant content. The attentional system exhibits domain-specific and sex-specific biases and attunements, probably rooted in evolutionary pressures to enhance reproductive and protective success. Emotional cues selectively capture covert attention based on their bio-social significance. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

    Science.gov (United States)

    Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

    2014-01-01

    Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

  16. A study of metaheuristic algorithms for high dimensional feature selection on microarray data

    Science.gov (United States)

    Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

    2017-11-01

    Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

  17. Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure.

    Science.gov (United States)

    Foroughi Pour, Ali; Dalton, Lori A

    2018-03-21

    Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms. The proposed algorithms outperform 2MNC-Robust and many other popular feature selection algorithms on synthetic data. In addition, enrichment analysis on real breast cancer, colon cancer, and Leukemia data indicates they also output many of the genes and pathways linked to the cancers under study. Bayesian feature selection is a promising framework for small-sample high-dimensional data, in particular biomarker discovery applications. When applied to cancer data these algorithms outputted many genes already shown to be involved in cancer as well as potentially new biomarkers. Furthermore, one of the proposed algorithms, SPM, outputs blocks of heavily correlated genes, particularly useful for studying gene interactions and gene networks.

  18. A New Feature Selection Algorithm Based on the Mean Impact Variance

    Directory of Open Access Journals (Sweden)

    Weidong Cheng

    2014-01-01

    Full Text Available The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2 the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA algorithm and a back propagation (BP network is constructed, and (4 the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates.

  19. On the interpretation and relevance of the Fundamental Theorem of Natural Selection.

    Science.gov (United States)

    Ewens, Warren J; Lessard, Sabin

    2015-09-01

    The attempt to understand the statement, and then to find the interpretation, of Fisher's "Fundamental Theorem of Natural Selection" caused problems for generations of population geneticists. Price's (1972) paper was the first to lead to an understanding of the statement of the theorem. The theorem shows (in the discrete-time case) that the so-called "partial change" in mean fitness of a population between a parental generation and an offspring generation is the parental generation additive genetic variance in fitness divided by the parental generation mean fitness. In the continuous-time case the partial rate of change in mean fitness is equal to the parental generation additive genetic variance in fitness with no division by the mean fitness. This "partial change" has been interpreted by some as the change in mean fitness due to changes in gene frequency, and by others as the change in mean fitness due to natural selection. (Fisher variously used both interpretations.) In this paper we discuss these interpretations of the theorem. We indicate why we are unhappy with both. We also discuss the long-term relevance of the Fundamental Theorem of Natural Selection, again reaching a negative assessment. We introduce and discuss the concept of genic evolutionary potential. We finally review an optimizing theorem that involves changes in gene frequency, the additive genetic variance in fitness and the mean fitness itself, all of which are involved in the Fundamental Theorem of Natural Selection, and which is free of the difficulties in interpretation of the Fundamental Theorem of Natural Selection. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma

    KAUST Repository

    Abusamra, Heba

    2013-11-01

    Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification. Interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This paper aims on a comparative study of state-of-the- art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k-nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t-statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used in the experiments. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.

  1. A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma

    KAUST Repository

    Abusamra, Heba

    2013-01-01

    Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification. Interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This paper aims on a comparative study of state-of-the- art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k-nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t-statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used in the experiments. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.

  2. Sparse Bayesian classification and feature selection for biological expression data with high correlations.

    Directory of Open Access Journals (Sweden)

    Xian Yang

    Full Text Available Classification models built on biological expression data are increasingly used to predict distinct disease subtypes. Selected features that separate sample groups can be the candidates of biomarkers, helping us to discover biological functions/pathways. However, three challenges are associated with building a robust classification and feature selection model: 1 the number of significant biomarkers is much smaller than that of measured features for which the search will be exhaustive; 2 current biological expression data are big in both sample size and feature size which will worsen the scalability of any search algorithms; and 3 expression profiles of certain features are typically highly correlated which may prevent to distinguish the predominant features. Unfortunately, most of the existing algorithms are partially addressing part of these challenges but not as a whole. In this paper, we propose a unified framework to address the above challenges. The classification and feature selection problem is first formulated as a nonconvex optimisation problem. Then the problem is relaxed and solved iteratively by a sequence of convex optimisation procedures which can be distributed computed and therefore allows the efficient implementation on advanced infrastructures. To illustrate the competence of our method over others, we first analyse a randomly generated simulation dataset under various conditions. We then analyse a real gene expression dataset on embryonal tumour. Further downstream analysis, such as functional annotation and pathway analysis, are performed on the selected features which elucidate several biological findings.

  3. Feature selection for Bayesian network classifiers using the MDL-FS score

    NARCIS (Netherlands)

    Drugan, Madalina M.; Wiering, Marco A.

    When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features

  4. Recursive Cluster Elimination (RCE for classification and feature selection from gene expression data

    Directory of Open Access Journals (Sweden)

    Showe Louise C

    2007-05-01

    Full Text Available Abstract Background Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE rather than recursive feature elimination (RFE. We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE. Results We have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs, a supervised machine learning classification method, to identify and score (rank those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA with recursive feature elimination (SVM-RFE and PDA-RFE are used to remove genes based on their individual discriminant weights. Conclusion SVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together

  5. Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

    Directory of Open Access Journals (Sweden)

    Liogienė Tatjana

    2016-07-01

    Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

  6. Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2013-01-01

    Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

  7. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Object-based selection from spatially-invariant representations: evidence from a feature-report task.

    Science.gov (United States)

    Matsukura, Michi; Vecera, Shaun P

    2011-02-01

    Attention selects objects as well as locations. When attention selects an object's features, observers identify two features from a single object more accurately than two features from two different objects (object-based effect of attention; e.g., Duncan, Journal of Experimental Psychology: General, 113, 501-517, 1984). Several studies have demonstrated that object-based attention can operate at a late visual processing stage that is independent of objects' spatial information (Awh, Dhaliwal, Christensen, & Matsukura, Psychological Science, 12, 329-334, 2001; Matsukura & Vecera, Psychonomic Bulletin & Review, 16, 529-536, 2009; Vecera, Journal of Experimental Psychology: General, 126, 14-18, 1997; Vecera & Farah, Journal of Experimental Psychology: General, 123, 146-160, 1994). In the present study, we asked two questions regarding this late object-based selection mechanism. In Part I, we investigated how observers' foreknowledge of to-be-reported features allows attention to select objects, as opposed to individual features. Using a feature-report task, a significant object-based effect was observed when to-be-reported features were known in advance but not when this advance knowledge was absent. In Part II, we examined what drives attention to select objects rather than individual features in the absence of observers' foreknowledge of to-be-reported features. Results suggested that, when there was no opportunity for observers to direct their attention to objects that possess to-be-reported features at the time of stimulus presentation, these stimuli must retain strong perceptual cues to establish themselves as separate objects.

  9. A Feature Subset Selection Method Based On High-Dimensional Mutual Information

    Directory of Open Access Journals (Sweden)

    Chee Keong Kwoh

    2011-04-01

    Full Text Available Feature selection is an important step in building accurate classifiers and provides better understanding of the data sets. In this paper, we propose a feature subset selection method based on high-dimensional mutual information. We also propose to use the entropy of the class attribute as a criterion to determine the appropriate subset of features when building classifiers. We prove that if the mutual information between a feature set X and the class attribute Y equals to the entropy of Y , then X is a Markov Blanket of Y . We show that in some cases, it is infeasible to approximate the high-dimensional mutual information with algebraic combinations of pairwise mutual information in any forms. In addition, the exhaustive searches of all combinations of features are prerequisite for finding the optimal feature subsets for classifying these kinds of data sets. We show that our approach outperforms existing filter feature subset selection methods for most of the 24 selected benchmark data sets.

  10. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Shahrbanoo Goli

    2016-01-01

    Full Text Available The Support Vector Regression (SVR model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model.

  11. Raman spectral feature selection using ant colony optimization for breast cancer diagnosis.

    Science.gov (United States)

    Fallahzadeh, Omid; Dehghani-Bidgoli, Zohreh; Assarian, Mohammad

    2018-06-04

    Pathology as a common diagnostic test of cancer is an invasive, time-consuming, and partially subjective method. Therefore, optical techniques, especially Raman spectroscopy, have attracted the attention of cancer diagnosis researchers. However, as Raman spectra contain numerous peaks involved in molecular bounds of the sample, finding the best features related to cancerous changes can improve the accuracy of diagnosis in this method. The present research attempted to improve the power of Raman-based cancer diagnosis by finding the best Raman features using the ACO algorithm. In the present research, 49 spectra were measured from normal, benign, and cancerous breast tissue samples using a 785-nm micro-Raman system. After preprocessing for removal of noise and background fluorescence, the intensity of 12 important Raman bands of the biological samples was extracted as features of each spectrum. Then, the ACO algorithm was applied to find the optimum features for diagnosis. As the results demonstrated, by selecting five features, the classification accuracy of the normal, benign, and cancerous groups increased by 14% and reached 87.7%. ACO feature selection can improve the diagnostic accuracy of Raman-based diagnostic models. In the present study, features corresponding to ν(C-C) αhelix proline, valine (910-940), νs(C-C) skeletal lipids (1110-1130), and δ(CH2)/δ(CH3) proteins (1445-1460) were selected as the best features in cancer diagnosis.

  12. A HYBRID FILTER AND WRAPPER FEATURE SELECTION APPROACH FOR DETECTING CONTAMINATION IN DRINKING WATER MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    S. VISALAKSHI

    2017-07-01

    Full Text Available Feature selection is an important task in predictive models which helps to identify the irrelevant features in the high - dimensional dataset. In this case of water contamination detection dataset, the standard wrapper algorithm alone cannot be applied because of the complexity. To overcome this computational complexity problem and making it lighter, filter-wrapper based algorithm has been proposed. In this work, reducing the feature space is a significant component of water contamination. The main findings are as follows: (1 The main goal is speeding up the feature selection process, so the proposed filter - based feature pre-selection is applied and guarantees that useful data are improbable to be detached in the initial stage which discussed briefly in this paper. (2 The resulting features are again filtered by using the Genetic Algorithm coded with Support Vector Machine method, where it facilitates to nutshell the subset of features with high accuracy and decreases the expense. Experimental results show that the proposed methods trim down redundant features effectively and achieved better classification accuracy.

  13. Comparison of feature selection and classification for MALDI-MS data

    Directory of Open Access Journals (Sweden)

    Yang Mary

    2009-07-01

    Full Text Available Abstract Introduction In the classification of Mass Spectrometry (MS proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE, and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC, Naïve Bayes Classifier (NBC, Nearest Mean Scaled Classifier (NMSC, uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing

  14. Features of selection of children for occupations by artistic gymnastics in modern Kurdistan

    OpenAIRE

    Abdulvahid Dlshad Nihad

    2015-01-01

    Purpose: to study the organizational and pedagogical conditions of selection of children for occupations existing in the republic Kurdistan artistic gymnastics Material and Methods: questioning of 24 trainers on artistic gymnastics and experts in physical culture of the republic Kurdistan was carried out. The general questions of selection and methodical features of selection of children for occupations by artistic gymnastics in Kurdistan were studied. Results: questioning revealed absence of...

  15. Enhancing Web Service Selection by User Preferences of Non-Functional Features

    OpenAIRE

    Badr , Youakim; Abraham , Ajith; Biennier , Frédérique; Grosan , Crina

    2008-01-01

    International audience; Selection of an appropriate Web service for a particular task has become a difficult challenge due to the increasing number of Web services offering similar functionalities. The functional properties describe what the service can do and the nonfunctional properties depict how the service can do it. Non-functional properties involving qualitative or quantitative features have become essential criteria to enhance the selection process of services making the selection pro...

  16. Feature selection from a facial image for distinction of sasang constitution.

    Science.gov (United States)

    Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

    2009-09-01

    Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

  17. Feature Selection from a Facial Image for Distinction of Sasang Constitution

    Directory of Open Access Journals (Sweden)

    Imhoi Koo

    2009-01-01

    Full Text Available Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

  18. Feature Selection from a Facial Image for Distinction of Sasang Constitution

    Science.gov (United States)

    Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

    2009-01-01

    Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

  19. An ant colony optimization based feature selection for web page classification.

    Science.gov (United States)

    Saraç, Esra; Özel, Selma Ayşe

    2014-01-01

    The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

  20. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

    2014-01-01

    Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

  1. Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

    Science.gov (United States)

    Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

    2013-01-01

    DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

  2. Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors

    Directory of Open Access Journals (Sweden)

    Hong Zhao

    2013-01-01

    Full Text Available Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal distribution measurement errors is constructed. With this model, coverings are constructed from data rather than assigned by users. Third, a new cost-sensitive feature selection problem is defined on this model. It is more realistic than the existing feature selection problems. Fourth, both backtracking and heuristic algorithms are proposed to deal with the new problem. Experimental results show the efficiency of the pruning techniques for the backtracking algorithm and the effectiveness of the heuristic algorithm. This study is a step toward realistic applications of the cost-sensitive learning.

  3. A selective overview of feature screening for ultrahigh-dimensional data.

    Science.gov (United States)

    JingYuan, Liu; Wei, Zhong; RunZe, L I

    2015-10-01

    High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many high-dimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.

  4. Infrared face recognition based on LBP histogram and KW feature selection

    Science.gov (United States)

    Xie, Zhihua

    2014-07-01

    The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

  5. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

    Science.gov (United States)

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  6. A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

    Science.gov (United States)

    Shahiri, Amirah Mohamed; Husain, Wahidah; Rashid, Nur'Aini Abd

    2017-10-01

    Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students' dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.

  7. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2015-01-01

    Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  8. Selected, annotated bibliography of studies relevant to the isolation of nuclear wastes

    International Nuclear Information System (INIS)

    Hyder, L.K.; Fore, C.S.; Vaughan, N.D.; Faust, R.A.

    1980-09-01

    This annotated bibliography of 705 references represents the first in a series to be published by the Ecological Sciences Information Center containing scientific, technical, economic, and regulatory information relevant to nuclear waste isolation. Most references discuss deep geologic disposal, with fewer studies of deep seabed disposal; space disposal is also included. The publication covers both domestic and foreign literature for the period 1954 to 1980. Major chapters selected are Chemical and Physical Aspects; Container Design and Performance; Disposal Site; Envirnmental Transport; General Studies and Reviews; Geology, Hydrology and Site Resources; Regulatory and Economic Aspects; Repository Design and Engineering; Transportation Technology; Waste Production; and Waste Treatment. Specialized data fields have been incorporated to improve the ease and accuracy of locating pertinent references. Specific radionuclides for which data are presented are listed in the Measured Radionuclides field, and specific parameters which affect the migration of these radionuclides are presented in the Measured Parameters field. The references within each chapter are arranged alphabetically by leading author, corporate affiliation, or title of the document. When the author is not given, the corporate affiliation appears first. If these two levels of authorship are not given, the title of the document is used as the identifying level. Indexes are provided for author(s), keywords, subject category, title, geographic location, measured parameters, measured radionuclides, and publication description

  9. EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

    Science.gov (United States)

    Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

    2017-07-01

    Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

  10. [Detection of biofilm formation by selected pathogens relevant to the food industry].

    Science.gov (United States)

    Šilhová-Hrušková, L; Moťková, P; Šilha, D; Vytřasová, J

    2015-09-01

    Detection of biofilm formation by microbial pathogens relevant to the food industry and comparison of biofilm formation under different conditions of culture. The following microorganisms were selected for the study: Staphylococcus aureus, Listeria innocua, Listeria ivanovii, Cronobacter sakazakii, Cronobacter muytjensii, Arcobacter butzleri, Arcobacter cryaerophilus, Campylobacter jejuni, and Campylobacter coli. To detect biofilm formation the microtiter plate assay, as described by Christensen and culture on stainless steel coupons were used. The biofilm forming capacity was confirmed in all microorganisms tested, both on the microtiter plates and stainless steel coupons. Biofilm formation was influenced by the culture medium, material used, and culture duration as well as by the test microorganism. It was found that different species and strains of the same genus differ in biofilm formation. Differences were also found between the collection strains and isolates from the environment. Some bacteria tended to form biofilm more readily on the surface of the polyethylene microtiter plates and less readily on stainless steel coupons while others appeared to have an opposite tendency. Some pathogens were able to increase the planktonic cell density in the initial suspension even by three orders of magnitude within 72 hours while producing plenty of biofilm. The study of biofilm formation by high risk pathogens is of utmost importance, not only to the food industry. From the obtained results, it is evident that bacterial biofilms form rapidly (within 24 hours in the present study). Due to their architecture, these biofilms are difficult to eradicate, and therefore, it is crucial to prevent biofilm formation.

  11. Selected, annotated bibliography of studies relevant to the isolation of nuclear wastes. [705 references

    Energy Technology Data Exchange (ETDEWEB)

    Hyder, L.K.; Fore, C.S.; Vaughan, N.D.; Faust, R.A.

    1980-09-01

    This annotated bibliography of 705 references represents the first in a series to be published by the Ecological Sciences Information Center containing scientific, technical, economic, and regulatory information relevant to nuclear waste isolation. Most references discuss deep geologic disposal, with fewer studies of deep seabed disposal; space disposal is also included. The publication covers both domestic and foreign literature for the period 1954 to 1980. Major chapters selected are Chemical and Physical Aspects; Container Design and Performance; Disposal Site; Envirnmental Transport; General Studies and Reviews; Geology, Hydrology and Site Resources; Regulatory and Economic Aspects; Repository Design and Engineering; Transportation Technology; Waste Production; and Waste Treatment. Specialized data fields have been incorporated to improve the ease and accuracy of locating pertinent references. Specific radionuclides for which data are presented are listed in the Measured Radionuclides field, and specific parameters which affect the migration of these radionuclides are presented in the Measured Parameters field. The references within each chapter are arranged alphabetically by leading author, corporate affiliation, or title of the document. When the author is not given, the corporate affiliation appears first. If these two levels of authorship are not given, the title of the document is used as the identifying level. Indexes are provided for author(s), keywords, subject category, title, geographic location, measured parameters, measured radionuclides, and publication description.

  12. Optimal Feature Space Selection in Detecting Epileptic Seizure based on Recurrent Quantification Analysis and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Saleh LAshkari

    2016-06-01

    Full Text Available Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA, it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.

  13. ELT Research in Turkey: A Content Analysis of Selected Features of Published Articles

    Science.gov (United States)

    Yagiz, Oktay; Aydin, Burcu; Akdemir, Ahmet Selçuk

    2016-01-01

    This study reviews a selected sample of 274 research articles on ELT, published between 2005 and 2015 in Turkish contexts. In the study, 15 journals in ULAKBIM database and articles from national and international journals accessed according to convenience sampling method were surveyed and relevant articles were obtained. A content analysis was…

  14. Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

    Science.gov (United States)

    Al-Rajab, Murad; Lu, Joan; Xu, Qiang

    2017-07-01

    This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Mining Specific and General Features in Both Positive and Negative Relevance Feedback. QUT E-Discovery Lab at the TREC󈧍 Relevance Feedback Track

    Science.gov (United States)

    2009-11-01

    relevance feedback algo- rithm. Four methods, εMap [1], MapA , P10A, and StatAP [2], were used in the track to measure the performance of Phase 2 runs...εMap and StatAP were applied to the runs us- ing the testing set of only ClueWeb09 Category-B, whereas MapA and P10A were applied to those using the...whole ClueWeb09 English set. Because our experiments were based on only ClueWeb09 Category-B, measuring our per- formance by MapA and P10A might not

  16. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

    Directory of Open Access Journals (Sweden)

    Himmelreich Uwe

    2009-07-01

    Full Text Available Abstract Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task.

  17. Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation.

    Directory of Open Access Journals (Sweden)

    Erica J Newton

    Full Text Available Woodland caribou (Rangifer tarandus caribou in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism-a compensatory functional response to anthropogenic

  18. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-20

    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

  19. A stereo remote sensing feature selection method based on artificial bee colony algorithm

    Science.gov (United States)

    Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

    2014-05-01

    To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

  20. Feature selection and multi-kernel learning for sparse representation on a manifold

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-03-01

    Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

  1. Feature selection and multi-kernel learning for sparse representation on a manifold.

    Science.gov (United States)

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2014-03-01

    Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes

    Science.gov (United States)

    2011-06-01

    implementing, and evaluating many feature selection algorithms. Mucciardi and Gose compared seven different techniques for choosing subsets of pattern...122 THIS PAGE INTENTIONALLY LEFT BLANK 123 LIST OF REFERENCES [1] A. Mucciardi and E. Gose , “A comparison of seven techniques for

  3. Selection of morphological features of pollen grains for chosen tree taxa

    Directory of Open Access Journals (Sweden)

    Agnieszka Kubik-Komar

    2018-05-01

    Full Text Available The basis of aerobiological studies is to monitor airborne pollen concentrations and pollen season timing. This task is performed by appropriately trained staff and is difficult and time consuming. The goal of this research is to select morphological characteristics of grains that are the most discriminative for distinguishing between birch, hazel and alder taxa and are easy to determine automatically from microscope images. This selection is based on the split attributes of the J4.8 classification trees built for different subsets of features. Determining the discriminative features by this method, we provide specific rules for distinguishing between individual taxa, at the same time obtaining a high percentage of correct classification. The most discriminative among the 13 morphological characteristics studied are the following: number of pores, maximum axis, minimum axis, axes difference, maximum oncus width, and number of lateral pores. The classification result of the tree based on this subset is better than the one built on the whole feature set and it is almost 94%. Therefore, selection of attributes before tree building is recommended. The classification results for the features easiest to obtain from the image, i.e. maximum axis, minimum axis, axes difference, and number of lateral pores, are only 2.09 pp lower than those obtained for the complete set, but 3.23 pp lower than the results obtained for the selected most discriminating attributes only.

  4. Data Visualization and Feature Selection Methods in Gel-based Proteomics

    DEFF Research Database (Denmark)

    Silva, Tomé Santos; Richard, Nadege; Dias, Jorge P.

    2014-01-01

    -based proteomics, summarizing the current state of research within this field. Particular focus is given on discussing the usefulness of available multivariate analysis tools both for data visualization and feature selection purposes. Visual examples are given using a real gel-based proteomic dataset as basis....

  5. Feature selection based on SVM significance maps for classification of dementia

    NARCIS (Netherlands)

    E.E. Bron (Esther); M. Smits (Marion); J.C. van Swieten (John); W.J. Niessen (Wiro); S. Klein (Stefan)

    2014-01-01

    textabstractSupport vector machine significance maps (SVM p-maps) previously showed clusters of significantly different voxels in dementiarelated brain regions. We propose a novel feature selection method for classification of dementia based on these p-maps. In our approach, the SVM p-maps are

  6. Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

    DEFF Research Database (Denmark)

    Prasad, Swati; Tan, Zheng-Hua; Prasad, Ramjee

    2011-01-01

    Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we...

  7. The Influence of Selected Personality and Workplace Features on Burnout among Nurse Academics

    Science.gov (United States)

    Kizilci, Sevgi; Erdogan, Vesile; Sozen, Emine

    2012-01-01

    This study aimed to determine the influence of selected individual and situational features on burnout among nurse academics. The Maslach Burnout Inventory was used to assess the burnout levels of academics. The sample population comprised 94 female participant. The emotion exhaustion (EE) score of the nurse academics was 16.43[plus or minus]5.97,…

  8. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...

  9. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

    Directory of Open Access Journals (Sweden)

    Aiming Liu

    2017-11-01

    Full Text Available Motor Imagery (MI electroencephalography (EEG is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP and local characteristic-scale decomposition (LCD algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.

  10. Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units

    Directory of Open Access Journals (Sweden)

    Fatemeh Afghah

    2018-03-01

    Full Text Available Intensive Care Units (ICUs are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies.

  11. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

    Science.gov (United States)

    Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

    2017-11-08

    Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

  12. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

    Science.gov (United States)

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

  13. EEG-based recognition of video-induced emotions: selecting subject-independent feature set.

    Science.gov (United States)

    Kortelainen, Jukka; Seppänen, Tapio

    2013-01-01

    Emotions are fundamental for everyday life affecting our communication, learning, perception, and decision making. Including emotions into the human-computer interaction (HCI) could be seen as a significant step forward offering a great potential for developing advanced future technologies. While the electrical activity of the brain is affected by emotions, offers electroencephalogram (EEG) an interesting channel to improve the HCI. In this paper, the selection of subject-independent feature set for EEG-based emotion recognition is studied. We investigate the effect of different feature sets in classifying person's arousal and valence while watching videos with emotional content. The classification performance is optimized by applying a sequential forward floating search algorithm for feature selection. The best classification rate (65.1% for arousal and 63.0% for valence) is obtained with a feature set containing power spectral features from the frequency band of 1-32 Hz. The proposed approach substantially improves the classification rate reported in the literature. In future, further analysis of the video-induced EEG changes including the topographical differences in the spectral features is needed.

  14. Human activity recognition based on feature selection in smart home using back-propagation algorithm.

    Science.gov (United States)

    Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

    2014-09-01

    In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Iwan Syarif

    2016-12-01

    Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.

  16. TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

    Directory of Open Access Journals (Sweden)

    A. Shamsoddini

    2017-09-01

    Full Text Available Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  17. Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

    Science.gov (United States)

    Shamsoddini, A.; Aboodi, M. R.; Karami, J.

    2017-09-01

    Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  18. Features of selection of children for occupations by artistic gymnastics in modern Kurdistan

    Directory of Open Access Journals (Sweden)

    Abdulvahid Dlshad Nihad

    2015-10-01

    Full Text Available Purpose: to study the organizational and pedagogical conditions of selection of children for occupations existing in the republic Kurdistan artistic gymnastics Material and Methods: questioning of 24 trainers on artistic gymnastics and experts in physical culture of the republic Kurdistan was carried out. The general questions of selection and methodical features of selection of children for occupations by artistic gymnastics in Kurdistan were studied. Results: questioning revealed absence of the general approved tests and scientific recommendations concerning their use, dependence of quality of selection on experience of the trainer. Conclusions: experts in the field of physical culture and sport consider inefficient the existing system of selection of children for occupations artistic gymnastics in Kurdistan; gymnastics coaches consider necessary testing’s at children of a level of development of flexibility, dexterity, abilities to manifestation of dynamic force and preservation of dynamic balance

  19. Selective weighting of action-related feature dimensions in visual working memory.

    Science.gov (United States)

    Heuer, Anna; Schubö, Anna

    2017-08-01

    Planning an action primes feature dimensions that are relevant for that particular action, increasing the impact of these dimensions on perceptual processing. Here, we investigated whether action planning also affects the short-term maintenance of visual information. In a combined memory and movement task, participants were to memorize items defined by size or color while preparing either a grasping or a pointing movement. Whereas size is a relevant feature dimension for grasping, color can be used to localize the goal object and guide a pointing movement. The results showed that memory for items defined by size was better during the preparation of a grasping movement than during the preparation of a pointing movement. Conversely, memory for color tended to be better when a pointing movement rather than a grasping movement was being planned. This pattern was not only observed when the memory task was embedded within the preparation period of the movement, but also when the movement to be performed was only indicated during the retention interval of the memory task. These findings reveal that a weighting of information in visual working memory according to action relevance can even be implemented at the representational level during maintenance, demonstrating that our actions continue to influence visual processing beyond the perceptual stage.

  20. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar

    2011-08-17

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  1. Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

    Directory of Open Access Journals (Sweden)

    Junbao Zheng

    2012-03-01

    Full Text Available Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor as well as its parallel channels (inner factor. The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

  2. Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

    Directory of Open Access Journals (Sweden)

    Taigang Liu

    2015-12-01

    Full Text Available The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE. These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

  3. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    Science.gov (United States)

    Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

  4. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

    2011-01-01

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  5. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    Science.gov (United States)

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

  6. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

    Science.gov (United States)

    Zhu, Xiaofeng; Suk, Heung-Il; Wang, Li; Lee, Seong-Whan; Shen, Dinggang

    2017-05-01

    In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ 2,1 -norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring

    Directory of Open Access Journals (Sweden)

    S. Borguet

    2008-01-01

    the essential elements of the sensor selection problem is defined from the Fisher information matrix. An example application consisting in a commercial turbofan engine illustrates the enhancement that can be expected from a wise selection of the sensor set.

  8. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

    Directory of Open Access Journals (Sweden)

    Lintao Yang

    2018-01-01

    Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

  9. A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection.

    Science.gov (United States)

    Ceccarelli, Michele; d'Acierno, Antonio; Facchiano, Angelo

    2009-10-15

    Mass spectrometry spectra, widely used in proteomics studies as a screening tool for protein profiling and to detect discriminatory signals, are high dimensional data. A large number of local maxima (a.k.a. peaks) have to be analyzed as part of computational pipelines aimed at the realization of efficient predictive and screening protocols. With this kind of data dimensions and samples size the risk of over-fitting and selection bias is pervasive. Therefore the development of bio-informatics methods based on unsupervised feature extraction can lead to general tools which can be applied to several fields of predictive proteomics. We propose a method for feature selection and extraction grounded on the theory of multi-scale spaces for high resolution spectra derived from analysis of serum. Then we use support vector machines for classification. In particular we use a database containing 216 samples spectra divided in 115 cancer and 91 control samples. The overall accuracy averaged over a large cross validation study is 98.18. The area under the ROC curve of the best selected model is 0.9962. We improved previous known results on the problem on the same data, with the advantage that the proposed method has an unsupervised feature selection phase. All the developed code, as MATLAB scripts, can be downloaded from http://medeaserver.isa.cnr.it/dacierno/spectracode.htm.

  10. A kernel-based multivariate feature selection method for microarray data classification.

    Directory of Open Access Journals (Sweden)

    Shiquan Sun

    Full Text Available High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, [Formula: see text]-nearest neighbor on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma.

  11. Selecting relevant and feasible measurement instruments for the revised Dutch clinical practice guideline for physical therapy in patients after stroke

    NARCIS (Netherlands)

    Otterman, Nicoline; Veerbeek, Janne; Schiemanck, Sven; van der Wees, Philip; Nollet, Frans; Kwakkel, Gert

    2017-01-01

    Purpose: To select relevant and feasible instruments for the revision of the Dutch clinical practice guideline for physical therapy in patients with stroke. Methods: In this implementation study a comprehensive proposal for ICF categories and matching instruments was developed, based on reliability

  12. Selecting relevant and feasible measurement instruments for the revised Dutch clinical practice guideline for physical therapy in patients after stroke

    NARCIS (Netherlands)

    Otterman, N.; Veerbeek, J.; Schiemanck, S.; Wees, P.J. van der; Nollet, F.; Kwakkel, G.

    2017-01-01

    PURPOSE: To select relevant and feasible instruments for the revision of the Dutch clinical practice guideline for physical therapy in patients with stroke. METHODS: In this implementation study a comprehensive proposal for ICF categories and matching instruments was developed, based on reliability

  13. The Effects of Topic Familiarity, Author Expertise, and Content Relevance on Norwegian Students' Document Selection: A Mixed Methods Study

    Science.gov (United States)

    McCrudden, Matthew T.; Stenseth, Tonje; Bråten, Ivar; Strømsø, Helge I.

    2016-01-01

    This mixed methods study investigated the extent to which author expertise and content relevance were salient to secondary Norwegian students (N = 153) when they selected documents that pertained to more familiar and less familiar topics. Quantitative results indicated that author expertise was more salient for the less familiar topic (nuclear…

  14. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2005-11-01

    Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

  15. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  16. Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

    Science.gov (United States)

    Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

    2014-03-01

    Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  17. Inference for feature selection using the Lasso with high-dimensional data

    DEFF Research Database (Denmark)

    Brink-Jensen, Kasper; Ekstrøm, Claus Thorn

    2014-01-01

    Penalized regression models such as the Lasso have proved useful for variable selection in many fields - especially for situations with high-dimensional data where the numbers of predictors far exceeds the number of observations. These methods identify and rank variables of importance but do...... not generally provide any inference of the selected variables. Thus, the variables selected might be the "most important" but need not be significant. We propose a significance test for the selection found by the Lasso. We introduce a procedure that computes inference and p-values for features chosen...... by the Lasso. This method rephrases the null hypothesis and uses a randomization approach which ensures that the error rate is controlled even for small samples. We demonstrate the ability of the algorithm to compute $p$-values of the expected magnitude with simulated data using a multitude of scenarios...

  18. A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

    Science.gov (United States)

    Zhang, Bin; Deng, Congying; Zhang, Yi

    2018-03-01

    Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

  19. Culturally Relevant Cyberbullying Prevention

    OpenAIRE

    Phillips, Gregory John

    2017-01-01

    In this action research study, I, along with a student intervention committee of 14 members, developed a cyberbullying intervention for a large urban high school on the west coast. This high school contained a predominantly African American student population. I aimed to discover culturally relevant cyberbullying prevention strategies for African American students. The intervention committee selected video safety messages featuring African American actors as the most culturally relevant cyber...

  20. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    International Nuclear Information System (INIS)

    Hinders, Mark K.; Miller, Corey A.

    2014-01-01

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy

  1. A hybrid approach to select features and classify diseases based on medical data

    Science.gov (United States)

    AbdelLatif, Hisham; Luo, Jiawei

    2018-03-01

    Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms

  2. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

    Science.gov (United States)

    Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

    2018-01-01

    The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

  3. An electronic image processing device featuring continuously selectable two-dimensional bipolar filter functions and real-time operation

    International Nuclear Information System (INIS)

    Charleston, B.D.; Beckman, F.H.; Franco, M.J.; Charleston, D.B.

    1981-01-01

    A versatile electronic-analogue image processing system has been developed for use in improving the quality of various types of images with emphasis on those encountered in experimental and diagnostic medicine. The operational principle utilizes spatial filtering which selectively controls the contrast of an image according to the spatial frequency content of relevant and non-relevant features of the image. Noise can be reduced or eliminated by selectively lowering the contrast of information in the high spatial frequency range. Edge sharpness can be enhanced by accentuating the upper midrange spatial frequencies. Both methods of spatial frequency control may be adjusted continuously in the same image to obtain maximum visibility of the features of interest. A precision video camera is used to view medical diagnostic images, either prints, transparencies or CRT displays. The output of the camera provides the analogue input signal for both the electronic processing system and the video display of the unprocessed image. The video signal input to the electronic processing system is processed by a two-dimensional spatial convolution operation. The system employs charged-coupled devices (CCDs), both tapped analogue delay lines (TADs) and serial analogue delay lines (SADs), to store information in the form of analogue potentials which are constantly being updated as new sampled analogue data arrive at the input. This information is convolved with a programmed bipolar radially symmetrical hexagonal function which may be controlled and varied at each radius by the operator in real-time by adjusting a set of front panel controls or by a programmed microprocessor control. Two TV monitors are used, one for processed image display and the other for constant reference to the original image. The working prototype has a full-screen display matrix size of 200 picture elements per horizontal line by 240 lines. The matrix can be expanded vertically and horizontally for the

  4. The Research and Application of SURF Algorithm Based on Feature Point Selection Algorithm

    Directory of Open Access Journals (Sweden)

    Zhang Fang Hu

    2014-04-01

    Full Text Available As the pixel information of depth image is derived from the distance information, when implementing SURF algorithm with KINECT sensor for static sign language recognition, there can be some mismatched pairs in palm area. This paper proposes a feature point selection algorithm, by filtering the SURF feature points step by step based on the number of feature points within adaptive radius r and the distance between the two points, it not only greatly improves the recognition rate, but also ensures the robustness under the environmental factors, such as skin color, illumination intensity, complex background, angle and scale changes. The experiment results show that the improved SURF algorithm can effectively improve the recognition rate, has a good robustness.

  5. Feature and score fusion based multiple classifier selection for iris recognition.

    Science.gov (United States)

    Islam, Md Rabiul

    2014-01-01

    The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

  6. featsel: A framework for benchmarking of feature selection algorithms and cost functions

    OpenAIRE

    Marcelo S. Reis; Gustavo Estrela; Carlos Eduardo Ferreira; Junior Barrera

    2017-01-01

    In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space as a Boolean lattice and has its core coded in C++ for computational efficiency purposes. Moreover, featsel includes Perl scripts to add new algorithms and/or cost functions, generate random instances, plot graphs and organize results into tables. Besides, this framework already comes with dozens of algorithms and co...

  7. A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification

    Science.gov (United States)

    Wang, Suge; Li, Deyu; Wei, Yingjie; Li, Hongxia

    With the rapid growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they intend to buy some product. As the reviews are often too many for customers to go through, how to automatically classify them into different sentiment orientation categories (i.e. positive/negative) has become a research problem. In this paper, based on Fisher's discriminant ratio, an effective feature selection method is proposed for product review text sentiment classification. In order to validate the validity of the proposed method, we compared it with other methods respectively based on information gain and mutual information while support vector machine is adopted as the classifier. In this paper, 6 subexperiments are conducted by combining different feature selection methods with 2 kinds of candidate feature sets. Under 1006 review documents of cars, the experimental results indicate that the Fisher's discriminant ratio based on word frequency estimation has the best performance with F value 83.3% while the candidate features are the words which appear in both positive and negative texts.

  8. Comparisons and Selections of Features and Classifiers for Short Text Classification

    Science.gov (United States)

    Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

    2017-10-01

    Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

  9. Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics.

    Science.gov (United States)

    Komeili, Majid; Louis, Wael; Armanfard, Narges; Hatzinakos, Dimitrios

    2018-05-01

    Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

  10. A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure.

    Science.gov (United States)

    Naghibi, Tofigh; Hoffmann, Sarah; Pfister, Beat

    2015-08-01

    Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximum-cut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.

  11. Effective automated feature construction and selection for classification of biological sequences.

    Directory of Open Access Journals (Sweden)

    Uday Kamath

    Full Text Available Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.We present an algorithmic framework (EFFECT for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification

  12. Fisher Information Based Meteorological Factors Introduction and Features Selection for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Shuping Cai

    2018-03-01

    Full Text Available Weather information is an important factor in short-term load forecasting (STLF. However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introduction and variable selection in STLF. Fisher information computation for one-dimensional and multidimensional weather variables is first described, and then the introduction of meteorological factors and variables selection for STLF models are discussed in detail. On this basis, different forecasting models with the proposed methodology are established. The proposed methodology is implemented on real data obtained from Electric Power Utility of Zhenjiang, Jiangsu Province, in southeast China. The results show the advantages of the proposed methodology in comparison with other traditional ones regarding prediction accuracy, and it has very good practical significance. Therefore, it can be used as a unified method for introducing weather variables into STLF models, and selecting their features.

  13. Feature-selective attention: evidence for a decline in old age.

    Science.gov (United States)

    Quigley, Cliodhna; Andersen, Søren K; Schulze, Lars; Grunwald, Martin; Müller, Matthias M

    2010-04-19

    Although attention in older adults is an active research area, feature-selective aspects have not yet been explicitly studied. Here we report the results of an exploratory study involving directed changes in feature-selective attention. The stimuli used were two random dot kinematograms (RDKs) of different colours, superimposed and centrally presented. A colour cue with random onset after the beginning of each trial instructed young and older subjects to attend to one of the RDKs and detect short intervals of coherent motion while ignoring analogous motion events in the non-cued RDK. Behavioural data show that older adults could detect motion, but discriminated target from distracter motion less reliably than young adults. The method of frequency tagging allowed us to separate the EEG responses to the attended and ignored stimuli and directly compare steady-state visual evoked potential (SSVEP) amplitudes elicited by each stimulus before and after cue onset. We found that younger adults show a clear attentional enhancement of SSVEP amplitude in the post-cue interval, while older adults' SSVEP responses to attended and ignored stimuli do not differ. Thus, in situations where attentional selection cannot be spatially resolved, older adults show a deficit in selection that is not shared by young adults. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

  14. Genetic Fuzzy System (GFS based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis

    Directory of Open Access Journals (Sweden)

    Meenakshi M. Pawar

    2016-09-01

    Full Text Available Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF using Genetic Fuzzy System (GFS in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

  15. How the risky features of previous selection affect subsequent decision-making: evidence from behavioral and fMRI measures.

    Science.gov (United States)

    Dong, Guangheng; Zhang, Yifen; Xu, Jiaojing; Lin, Xiao; Du, Xiaoxia

    2015-01-01

    Human decision making is rarely conducted in temporal isolation. It is often biased and affected by environmental variables, particularly prior selections. In this study, we used a task that simulates a real gambling process to explore the effect of the risky features of a previous selection on subsequent decision making. Compared with decision making after an advantageous risk-taking situation (Risk_Adv), that after a disadvantageous risk-taking situation (Risk_Disadv) is associated with a longer response time (RT, the time spent in making decisions) and higher brain activations in the caudate and the dorsolateral prefrontal cortex (DLPFC). Compared with decisions after Risk_Adv, those after Risk_Disadv in loss trials are associated with higher brain activations in the left superior temporal gyrus (STG) and the precuneus. Brain activity and relevant RTs significantly correlated. Overall, people who experience disadvantageous risk-taking selections tend to focus on current decision making and engage cognitive endeavors in value evaluation and in the regulation of their risk-taking behaviors during decision making.

  16. HEART RATE VARIABILITY CLASSIFICATION USING SADE-ELM CLASSIFIER WITH BAT FEATURE SELECTION

    Directory of Open Access Journals (Sweden)

    R Kavitha

    2017-07-01

    Full Text Available The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient’s cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient’s body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self- Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN to be able to increase the accuracy of the effect for HRV transmission classification. Hence, it is observed that the experiment carried out unveils that the precision is improved by the SADE-ELM method and concurrently optimizes the computation time.

  17. Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control

    Directory of Open Access Journals (Sweden)

    Adenike A. Adewuyi

    2016-10-01

    Full Text Available Pattern recognition-based myoelectric control of upper limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1 the performance of non-linear and linear pattern recognition algorithms and (2 the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p<0.001 but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p=0.07, intrinsic (p=0.06, or combined extrinsic and intrinsic muscle EMG (p=0.08, respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p<0.001 and time domain/autoregressive feature sets (p<0.01. This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

  18. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

    Directory of Open Access Journals (Sweden)

    Harris Lyndsay N

    2006-04-01

    Full Text Available Abstract Background Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results We developed a recursive support vector machine (R-SVM algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE, paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

  19. Using Confidence Interval-Based Estimation of Relevance to Select Social-Cognitive Determinants for Behavior Change Interventions

    Directory of Open Access Journals (Sweden)

    Rik Crutzen

    2017-07-01

    Full Text Available When developing an intervention aimed at behavior change, one of the crucial steps in the development process is to select the most relevant social-cognitive determinants. These determinants can be seen as the buttons one needs to push to establish behavior change. Insight into these determinants is needed to select behavior change methods (i.e., general behavior change techniques that are applied in an intervention in the development process. Therefore, a study on determinants is often conducted as formative research in the intervention development process. Ideally, all relevant determinants identified in such a study are addressed by an intervention. However, when developing a behavior change intervention, there are limits in terms of, for example, resources available for intervention development and the amount of content that participants of an intervention can be exposed to. Hence, it is important to select those determinants that are most relevant to the target behavior as these determinants should be addressed in an intervention. The aim of the current paper is to introduce a novel approach to select the most relevant social-cognitive determinants and use them in intervention development. This approach is based on visualization of confidence intervals for the means and correlation coefficients for all determinants simultaneously. This visualization facilitates comparison, which is necessary when making selections. By means of a case study on the determinants of using a high dose of 3,4-methylenedioxymethamphetamine (commonly known as ecstasy, we illustrate this approach. We provide a freely available tool to facilitate the analyses needed in this approach.

  20. Using Confidence Interval-Based Estimation of Relevance to Select Social-Cognitive Determinants for Behavior Change Interventions.

    Science.gov (United States)

    Crutzen, Rik; Peters, Gjalt-Jorn Ygram; Noijen, Judith

    2017-01-01

    When developing an intervention aimed at behavior change, one of the crucial steps in the development process is to select the most relevant social-cognitive determinants. These determinants can be seen as the buttons one needs to push to establish behavior change. Insight into these determinants is needed to select behavior change methods (i.e., general behavior change techniques that are applied in an intervention) in the development process. Therefore, a study on determinants is often conducted as formative research in the intervention development process. Ideally, all relevant determinants identified in such a study are addressed by an intervention. However, when developing a behavior change intervention, there are limits in terms of, for example, resources available for intervention development and the amount of content that participants of an intervention can be exposed to. Hence, it is important to select those determinants that are most relevant to the target behavior as these determinants should be addressed in an intervention. The aim of the current paper is to introduce a novel approach to select the most relevant social-cognitive determinants and use them in intervention development. This approach is based on visualization of confidence intervals for the means and correlation coefficients for all determinants simultaneously. This visualization facilitates comparison, which is necessary when making selections. By means of a case study on the determinants of using a high dose of 3,4-methylenedioxymethamphetamine (commonly known as ecstasy), we illustrate this approach. We provide a freely available tool to facilitate the analyses needed in this approach.

  1. Selection of relevant items for decommissioning costing estimation of a PWR using fuzzy logic

    International Nuclear Information System (INIS)

    Monteiro, Deiglys Borges; Busse, Alexander Lucas; Moreira, Joao M.L.; Maiorino, Jose Rubens

    2015-01-01

    The decommissioning is an important part of a nuclear power plant life cycle which may occur by technical, economical or safety reasons. Decommissioning requires carrying out a large number of tasks that should be planned in advance, involves cost evaluations, preparation of plans of activity and actual operational actions. Despite the large number of tasks, only part of them is relevant for cost estimation purpose. The technical literature and international regulatory agencies suggest a variety of methods for decommissioning cost estimation. Most of them require a very detailed knowledge of the plant and data available suitable for plants that are starting their decommissioning but not for those in the planning stage. The present work aims to apply fuzzy logic to sort out relevant items to cost estimation in order to reduce the work effort involved. The scheme uses parametric equations for specific cost items, and is applied to specific parts of the process of nuclear power plant decommissioning. (author)

  2. Selection of relevant items for decommissioning costing estimation of a PWR using fuzzy logic

    Energy Technology Data Exchange (ETDEWEB)

    Monteiro, Deiglys Borges; Busse, Alexander Lucas; Moreira, Joao M.L.; Maiorino, Jose Rubens, E-mail: deiglys.monteiro@ufabc.edu.br, E-mail: alexlucasb@gmail.com, E-mail: joao.moreira@ufabc.edu.br, E-mail: joserubens.maiorino@ufabc.edu.br [Universidade Federal do ABC (CECS/UFABC), Santo Andre, SP (Brazil). Centro de Engenharia, Modelagem e Ciencias Aplicadas. Programa de Pos-Graduacao em Energia e Engenharia da Energia

    2015-07-01

    The decommissioning is an important part of a nuclear power plant life cycle which may occur by technical, economical or safety reasons. Decommissioning requires carrying out a large number of tasks that should be planned in advance, involves cost evaluations, preparation of plans of activity and actual operational actions. Despite the large number of tasks, only part of them is relevant for cost estimation purpose. The technical literature and international regulatory agencies suggest a variety of methods for decommissioning cost estimation. Most of them require a very detailed knowledge of the plant and data available suitable for plants that are starting their decommissioning but not for those in the planning stage. The present work aims to apply fuzzy logic to sort out relevant items to cost estimation in order to reduce the work effort involved. The scheme uses parametric equations for specific cost items, and is applied to specific parts of the process of nuclear power plant decommissioning. (author)

  3. Studies of climatic effects relevant to site selection and to assessments of the radiological impact of disposal at selected sites

    International Nuclear Information System (INIS)

    Goodess, C.M.; Palutikof, J.P.; Davies, T.D.

    1988-07-01

    Projected timescales for the transport of radionuclides from an undisturbed underground nuclear waste repository to the surface are in the range of tens of thousands to millions of years. Over these timescales major natural and anthropogenic changes in climate can be expected. As part of the UK safety assessment programme time-dependent models of the repository environment are being developed and the Climatic Research Unit (CRU) has undertaken a study of relevant climatic change and climatic effects. In order to estimate the probable range succession and duration of major climate states likely to be experienced in the UK over the next 10 6 years the nature and mechanisms of climatic variations in the past are first reviewed. (Author)

  4. Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.

    Science.gov (United States)

    Sahran, Shahnorbanun; Albashish, Dheeb; Abdullah, Azizi; Shukor, Nordashima Abd; Hayati Md Pauzi, Suria

    2018-04-18

    Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components. We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC. We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods. We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to

  5. Many roads may lead to Rome: Selected features of quality control within environmental assessment systems in the US, NL, CA, and UK

    Energy Technology Data Exchange (ETDEWEB)

    Günther, Markus, E-mail: markus.guenther@tu-berlin.de; Geißler, Gesa; Köppel, Johann

    2017-01-15

    As there is no one-and-only concept on how to precisely define and establish quality control (QC) or quality assurance (QA) in the making of environmental assessments (EA), this paper presents selected features of international approaches that address quality in EA systems in the USA, the Netherlands, Canada, and the United Kingdom. Based on explanative case studies, we highlight the embedding of specific quality control features within the EA systems, the objectives and processes, and relevant transparency challenges. Such features of QC/QA approaches can be considered in cases where substantial quality control and assurance efforts are still missing. Yet further research needs to be conducted on the efficacy of these approaches, which remains beyond the scope of this study. - Highlights: • We present four tools for quality control and assurance from different EA systems. • Approaches vary in institutional setting, objectives, procedures, and transparency. • Highlighted features might provide guidance in cases where QC/QA is still lacking.

  6. Feature selection for disruption prediction from scratch in JET by using genetic algorithms and probabilistic predictors

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Augusto, E-mail: augusto.pereira@ciemat.es [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Vega, Jesús; Moreno, Raúl [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Dormido-Canto, Sebastián [Dpto. Informática y Automática – UNED, Madrid (Spain); Rattá, Giuseppe A. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Pavón, Fernando [Dpto. Informática y Automática – UNED, Madrid (Spain)

    2015-10-15

    Recently, a probabilistic classifier has been developed at JET to be used as predictor from scratch. It has been applied to a database of 1237 JET ITER-like wall (ILW) discharges (of which 201 disrupted) with good results: success rate of 94% and false alarm rate of 4.21%. A combinatorial analysis between 14 features to ensure the selection of the best ones to achieve good enough results in terms of success rate and false alarm rate was performed. All possible combinations with a number of features between 2 and 7 were tested and 9893 different predictors were analyzed. An important drawback in this analysis was the time required to compute the results that can be estimated in 1731 h (∼2.4 months). Genetic algorithms (GA) are searching algorithms that simulate the process of natural selection. In this article, the GA and the Venn predictors are combined with the objective not only of finding good enough features within the 14 available ones but also of reducing the computational time requirements. Five different performance metrics as measures of the GA fitness function have been evaluated. The best metric was the measurement called Informedness, with just 6 generations (168 predictors at 29.4 h).

  7. Feature selection for disruption prediction from scratch in JET by using genetic algorithms and probabilistic predictors

    International Nuclear Information System (INIS)

    Pereira, Augusto; Vega, Jesús; Moreno, Raúl; Dormido-Canto, Sebastián; Rattá, Giuseppe A.; Pavón, Fernando

    2015-01-01

    Recently, a probabilistic classifier has been developed at JET to be used as predictor from scratch. It has been applied to a database of 1237 JET ITER-like wall (ILW) discharges (of which 201 disrupted) with good results: success rate of 94% and false alarm rate of 4.21%. A combinatorial analysis between 14 features to ensure the selection of the best ones to achieve good enough results in terms of success rate and false alarm rate was performed. All possible combinations with a number of features between 2 and 7 were tested and 9893 different predictors were analyzed. An important drawback in this analysis was the time required to compute the results that can be estimated in 1731 h (∼2.4 months). Genetic algorithms (GA) are searching algorithms that simulate the process of natural selection. In this article, the GA and the Venn predictors are combined with the objective not only of finding good enough features within the 14 available ones but also of reducing the computational time requirements. Five different performance metrics as measures of the GA fitness function have been evaluated. The best metric was the measurement called Informedness, with just 6 generations (168 predictors at 29.4 h).

  8. Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

    Science.gov (United States)

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-01-01

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

  9. Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

    Science.gov (United States)

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-07-30

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

  10. Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images

    Science.gov (United States)

    Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai

    2017-02-01

    It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.

  11. AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

    Science.gov (United States)

    Dasarathy, B. V.

    1976-01-01

    An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

  12. A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

    Science.gov (United States)

    Taşkin Kaya, Gülşen

    2013-10-01

    Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input

  13. Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

    Science.gov (United States)

    Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

    2014-01-01

    Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

  14. Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

    Science.gov (United States)

    Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

    We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

  15. iFER: facial expression recognition using automatically selected geometric eye and eyebrow features

    Science.gov (United States)

    Oztel, Ismail; Yolcu, Gozde; Oz, Cemil; Kazan, Serap; Bunyak, Filiz

    2018-03-01

    Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye-eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by ˜ 2.5 % compared to the best whole face facial recognition system while using only ˜ 1 / 3 of the facial region.

  16. Evaluation of three levels of selective devices relevant to management of the Danish Kattegat-Skagerrak Nephrops fishery

    DEFF Research Database (Denmark)

    Frandsen, Rikke; Holst, René; Madsen, Niels

    2009-01-01

    This study illuminates a range of technological options relevant to present legislation for regulating fish by-catch in a small-meshed Nephrops fishery. The selection of cod, haddock, hake, lemon sole, Nephrops, plaice, saithe, witch, and whiting were evaluated using the twin-trawl technique for...... parameters by species and confidence bands to compare the selective properties of different gear types. For cod, haddock, hake, Nephrops, plaice, and whiting we obtained estimates for all three gear variants, whereas we obtained estimates for lemon sole and witch only with the standard and the SMP codends...

  17. Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

    Directory of Open Access Journals (Sweden)

    Huamin Zhu

    2016-01-01

    Full Text Available Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection.

  18. Time course of spatial and feature selective attention for partly-occluded objects.

    Science.gov (United States)

    Kasai, Tetsuko; Takeya, Ryuji

    2012-07-01

    Attention selects objects/groups as the most fundamental units, and this may be achieved by an attention-spreading mechanism. Previous event-related potential (ERP) studies have found that attention-spreading is reflected by a decrease in the N1 spatial attention effect. The present study tested whether the electrophysiological attention effect is associated with the perception of object unity or amodal completion through the use of partly-occluded objects. ERPs were recorded in 14 participants who were required to pay attention to their left or right visual field and to press a button for a target shape in the attended field. Bilateral stimuli were presented rapidly, and were separated, connected, or connected behind an occluder. Behavioral performance in the connected and occluded conditions was worse than that in the separated condition, indicating that attention spread over perceptual object representations after amodal completion. Consistently, the late N1 spatial attention effect (180-220 ms post-stimulus) and the early phase (230-280 ms) of feature selection effects (target N2) at contralateral sites decreased, equally for the occluded and connected conditions, while the attention effect in the early N1 latency (140-180 ms) shifted most positively for the occluded condition. These results suggest that perceptual organization processes for object recognition transiently modulate spatial and feature selection processes in the visual cortex. Copyright © 2012 Elsevier Ltd. All rights reserved.

  19. A Hybrid Feature Subset Selection Algorithm for Analysis of High Correlation Proteomic Data

    Science.gov (United States)

    Kordy, Hussain Montazery; Baygi, Mohammad Hossein Miran; Moradi, Mohammad Hassan

    2012-01-01

    Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power. PMID:23717808

  20. Audio-visual synchrony and feature-selective attention co-amplify early visual processing.

    Science.gov (United States)

    Keitel, Christian; Müller, Matthias M

    2016-05-01

    Our brain relies on neural mechanisms of selective attention and converging sensory processing to efficiently cope with rich and unceasing multisensory inputs. One prominent assumption holds that audio-visual synchrony can act as a strong attractor for spatial attention. Here, we tested for a similar effect of audio-visual synchrony on feature-selective attention. We presented two superimposed Gabor patches that differed in colour and orientation. On each trial, participants were cued to selectively attend to one of the two patches. Over time, spatial frequencies of both patches varied sinusoidally at distinct rates (3.14 and 3.63 Hz), giving rise to pulse-like percepts. A simultaneously presented pure tone carried a frequency modulation at the pulse rate of one of the two visual stimuli to introduce audio-visual synchrony. Pulsed stimulation elicited distinct time-locked oscillatory electrophysiological brain responses. These steady-state responses were quantified in the spectral domain to examine individual stimulus processing under conditions of synchronous versus asynchronous tone presentation and when respective stimuli were attended versus unattended. We found that both, attending to the colour of a stimulus and its synchrony with the tone, enhanced its processing. Moreover, both gain effects combined linearly for attended in-sync stimuli. Our results suggest that audio-visual synchrony can attract attention to specific stimulus features when stimuli overlap in space.

  1. Discriminative region extraction and feature selection based on the combination of SURF and saliency

    Science.gov (United States)

    Deng, Li; Wang, Chunhong; Rao, Changhui

    2011-08-01

    The objective of this paper is to provide a possible optimization on salient region algorithm, which is extensively used in recognizing and learning object categories. Salient region algorithm owns the superiority of intra-class tolerance, global score of features and automatically prominent scale selection under certain range. However, the major limitation behaves on performance, and that is what we attempt to improve. By reducing the number of pixels involved in saliency calculation, it can be accelerated. We use interest points detected by fast-Hessian, the detector of SURF, as the candidate feature for saliency operation, rather than the whole set in image. This implementation is thereby called Saliency based Optimization over SURF (SOSU for short). Experiment shows that bringing in of such a fast detector significantly speeds up the algorithm. Meanwhile, Robustness of intra-class diversity ensures object recognition accuracy.

  2. Healthy full-term infants' brain responses to emotionally and linguistically relevant sounds using a multi-feature mismatch negativity (MMN) paradigm.

    Science.gov (United States)

    Kostilainen, Kaisamari; Wikström, Valtteri; Pakarinen, Satu; Videman, Mari; Karlsson, Linnea; Keskinen, Maria; Scheinin, Noora M; Karlsson, Hasse; Huotilainen, Minna

    2018-03-23

    We evaluated the feasibility of a multi-feature mismatch negativity (MMN) paradigm in studying auditory processing of healthy newborns. The aim was to examine the automatic change-detection and processing of semantic and emotional information in speech in newborns. Brain responses of 202 healthy newborns were recorded with a multi-feature paradigm including a Finnish bi-syllabic pseudo-word/ta-ta/as a standard stimulus, six linguistically relevant deviant stimuli and three emotionally relevant stimuli (happy, sad, angry). Clear responses to emotional sounds were found already at the early latency window 100-200 ms, whereas responses to linguistically relevant minor changes and emotional stimuli at the later latency window 300-500 ms did not reach significance. Moreover, significant interaction between gender and emotional stimuli was found in the early latency window. Further studies on using multi-feature paradigms with linguistic and emotional stimuli in newborns are needed, especially those containing of follow-ups, enabling the assessment of the predictive value of early variations between subjects. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Sungho Kim

    2016-07-01

    Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

  4. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Science.gov (United States)

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  5. Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

    Science.gov (United States)

    Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

    2013-01-01

    Accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.

  6. COMPREHENSIVE ANALYSIS OF BIOLOGICALLY RELEVANT ARSENICALS BY PH-SELECTIVE HYDRIDE GENERATION-ATOMIC ABSORPTION SPECTROMETRY

    Science.gov (United States)

    A method based on pH-selective generation and separation of arsines is commonly used for analysis of inorganic, methylated, and dimethylated trivalent and pentavalent arsenicals by hydride generation-atomic absorption spectrometry (HG-AAS). We have optimized this method to pe...

  7. Supramolecular Chemistry of Selective Anion Recognition for Anions of Environmental Relevance

    International Nuclear Information System (INIS)

    Bowman-James, K.; Wilson, G.; Moyer, B. A.

    2004-01-01

    This project involves the design and synthesis of receptors for oxoanions of environmental importance, including emphasis on high level and low activity waste. Target anions have included primarily oxoanions and a study of the basic concepts behind selective binding of target anions. A primary target has been sulfate because of its deleterious influence on the vitrification of tank wastes

  8. Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

    Directory of Open Access Journals (Sweden)

    Fei Dong

    2018-01-01

    Full Text Available In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT. In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL, was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

  9. Investigation of selected surface integrity features of duplex stainless steel (DSS after turning

    Directory of Open Access Journals (Sweden)

    G. Krolczyk

    2015-01-01

    Full Text Available The article presents surface roughness profiles and Abbott - Firestone curves with vertical and amplitude parameters of surface roughness after turning by means of a coated sintered carbide wedge with a coating with ceramic intermediate layer. The investigation comprised the influence of cutting speed on the selected features of surface integrity in dry machining. The material under investigation was duplex stainless steel with two-phase ferritic-austenitic structure. The tests have been performed under production conditions during machining of parts for electric motors and deep-well pumps. The obtained results allow to draw conclusions about the characteristics of surface properties of the machined parts.

  10. Improving feature selection process resistance to failures caused by curse-of-dimensionality effects

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Grim, Jiří; Novovičová, Jana; Pudil, P.

    2011-01-01

    Roč. 47, č. 3 (2011), s. 401-425 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection * curse of dimensionality * over-fitting * stability * machine learning * dimensionality reduction Subject RIV: IN - Informatics, Computer Science Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/RO/somol-0368741.pdf

  11. A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    Chunren Lai

    2017-12-01

    Full Text Available It is crucial to differentiate patients with temporal lobe epilepsy (TLE from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV, and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM, and the support vector machine-recursive feature elimination (SVM-RFE were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy, followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.

  12. An application of locally linear model tree algorithm with combination of feature selection in credit scoring

    Science.gov (United States)

    Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad

    2014-10-01

    Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.

  13. Mining for diagnostic information in body surface potential maps: A comparison of feature selection techniques

    Directory of Open Access Journals (Sweden)

    McCullagh Paul J

    2005-09-01

    Full Text Available Abstract Background In body surface potential mapping, increased spatial sampling is used to allow more accurate detection of a cardiac abnormality. Although diagnostically superior to more conventional electrocardiographic techniques, the perceived complexity of the Body Surface Potential Map (BSPM acquisition process has prohibited its acceptance in clinical practice. For this reason there is an interest in striking a compromise between the minimum number of electrocardiographic recording sites required to sample the maximum electrocardiographic information. Methods In the current study, several techniques widely used in the domains of data mining and knowledge discovery have been employed to mine for diagnostic information in 192 lead BSPMs. In particular, the Single Variable Classifier (SVC based filter and Sequential Forward Selection (SFS based wrapper approaches to feature selection have been implemented and evaluated. Using a set of recordings from 116 subjects, the diagnostic ability of subsets of 3, 6, 9, 12, 24 and 32 electrocardiographic recording sites have been evaluated based on their ability to correctly asses the presence or absence of Myocardial Infarction (MI. Results It was observed that the wrapper approach, using sequential forward selection and a 5 nearest neighbour classifier, was capable of choosing a set of 24 recording sites that could correctly classify 82.8% of BSPMs. Although the filter method performed slightly less favourably, the performance was comparable with a classification accuracy of 79.3%. In addition, experiments were conducted to show how (a features chosen using the wrapper approach were specific to the classifier used in the selection model, and (b lead subsets chosen were not necessarily unique. Conclusion It was concluded that both the filter and wrapper approaches adopted were suitable for guiding the choice of recording sites useful for determining the presence of MI. It should be noted however

  14. A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

    Directory of Open Access Journals (Sweden)

    Kursat Zuhtuogullari

    2013-01-01

    Full Text Available The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

  15. A soft computing based approach using modified selection strategy for feature reduction of medical systems.

    Science.gov (United States)

    Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

    2013-01-01

    The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

  16. Distance selection based on relevance feedback in the context of CBIR using the SFS meta-heuristic with one round

    Directory of Open Access Journals (Sweden)

    Mawloud Mosbah

    2017-03-01

    Full Text Available In this paper, we address the selection in the context of Content Based-Image Retrieval (CBIR. Instead of addressing features’ selection issue, we deal here with distance selection as a novel paradigm poorly addressed within CBIR field. Whereas distance concept is a very precise and sharp mathematical tool, we extend the study to weak distances: Similarity, quasi-distance, and divergence. Therefore, as many as eighteen (18 such measures as considered: distances: {Euclidian, …}, similarities{Ruzika, …}, quasi-distances: {Neyman-X2, …} and divergences: {Jeffrey, …}. We specifically propose a hybrid system based on the Sequential Forward Selector (SFS meta-heuristic with one round and relevance feedback. The experiments conducted on the Wang database (Corel-1K using color moments as a signature show that our system yields promising results in terms of effectiveness.

  17. Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features

    Directory of Open Access Journals (Sweden)

    Vishal eVaingankar

    2012-12-01

    Full Text Available All visual signals the cortex receives are influenced by the perigeniculate sector of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped centre-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate nonselective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate, using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-nonlinear models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs nonlinearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream

  18. Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

    Directory of Open Access Journals (Sweden)

    Md. Rabiul Islam

    2014-01-01

    Full Text Available The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

  19. Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters.

    Science.gov (United States)

    Dill, Vanderson; Klein, Pedro Costa; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

    2018-04-01

    Current state-of-the-art methods for whole and subfield hippocampus segmentation use pre-segmented templates, also known as atlases, in the pre-processing stages. Typically, the input image is registered to the template, which provides prior information for the segmentation process. Using a single standard atlas increases the difficulty in dealing with individuals who have a brain anatomy that is morphologically different from the atlas, especially in older brains. To increase the segmentation precision in these cases, without any manual intervention, multiple atlases can be used. However, registration to many templates leads to a high computational cost. Researchers have proposed to use an atlas pre-selection technique based on meta-information followed by the selection of an atlas based on image similarity. Unfortunately, this method also presents a high computational cost due to the image-similarity process. Thus, it is desirable to pre-select a smaller number of atlases as long as this does not impact on the segmentation quality. To pick out an atlas that provides the best registration, we evaluate the use of three meta-information parameters (medical condition, age range, and gender) to choose the atlas. In this work, 24 atlases were defined and each is based on the combination of the three meta-information parameters. These atlases were used to segment 352 vol from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Hippocampus segmentation with each of these atlases was evaluated and compared to reference segmentations of the hippocampus, which are available from ADNI. The use of atlas selection by meta-information led to a significant gain in the Dice similarity coefficient, which reached 0.68 ± 0.11, compared to 0.62 ± 0.12 when using only the standard MNI152 atlas. Statistical analysis showed that the three meta-information parameters provided a significant improvement in the segmentation accuracy. Copyright © 2018 Elsevier Ltd

  20. Highly selective deuteration of pharmaceutically relevant nitrogen-containing heterocycles: a flow chemistry approach.

    Science.gov (United States)

    Ötvös, Sándor B; Mándity, István M; Fülöp, Ferenc

    2011-08-01

    A simple and efficient flow-based technique is reported for the catalytic deuteration of several model nitrogen-containing heterocyclic compounds which are important building blocks of pharmacologically active materials. A continuous flow reactor was used in combination with on-demand pressure-controlled electrolytic D(2) production. The D(2) source was D(2)O, the consumption of which was very low. The experimental set-up allows the fine-tuning of pressure, temperature, and flow rate so as to determine the optimal conditions for the deuteration reactions. The described procedure lacks most of the drawbacks of the conventional batch deuteration techniques, and additionally is highly selective and reproducible.

  1. How to Select the most Relevant Roughness Parameters of a Surface: Methodology Research Strategy

    Science.gov (United States)

    Bobrovskij, I. N.

    2018-01-01

    In this paper, the foundations for new methodology creation which provides solving problem of surfaces structure new standards parameters huge amount conflicted with necessary actual floors quantity of surfaces structure parameters which is related to measurement complexity decreasing are considered. At the moment, there is no single assessment of the importance of a parameters. The approval of presented methodology for aerospace cluster components surfaces allows to create necessary foundation, to develop scientific estimation of surfaces texture parameters, to obtain material for investigators of chosen technological procedure. The methods necessary for further work, the creation of a fundamental reserve and development as a scientific direction for assessing the significance of microgeometry parameters are selected.

  2. Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features

    Science.gov (United States)

    Vaingankar, Vishal; Soto-Sanchez, Cristina; Wang, Xin; Sommer, Friedrich T.; Hirsch, Judith A.

    2012-01-01

    All visual signals the cortex receives are influenced by the perigeniculate sector (PGN) of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped center-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate non-selective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-non-linear (LN) models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs non-linearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream. PMID:23269915

  3. PREFACE: Special section featuring selected papers from the 3rd International Workshop on Numerical Modelling of High Temperature Superconductors Special section featuring selected papers from the 3rd International Workshop on Numerical Modelling of High Temperature Superconductors

    Science.gov (United States)

    Granados, Xavier; Sánchez, Àlvar; López-López, Josep

    2012-10-01

    The development of superconducting applications and superconducting engineering requires the support of consistent tools which can provide models for obtaining a good understanding of the behaviour of the systems and predict novel features. These models aim to compute the behaviour of the superconducting systems, design superconducting devices and systems, and understand and test the behavior of the superconducting parts. 50 years ago, in 1962, Charles Bean provided the superconducting community with a model efficient enough to allow the computation of the response of a superconductor to external magnetic fields and currents flowing through in an understandable way: the so called critical-state model. Since then, in addition to the pioneering critical-state approach, other tools have been devised for designing operative superconducting systems, allowing integration of the superconducting design in nearly standard electromagnetic computer-aided design systems by modelling the superconducting parts with consideration of time-dependent processes. In April 2012, Barcelona hosted the 3rd International Workshop on Numerical Modelling of High Temperature Superconductors (HTS), the third in a series of workshops started in Lausanne in 2010 and followed by Cambridge in 2011. The workshop reflected the state-of-the-art and the new initiatives of HTS modelling, considering mathematical, physical and technological aspects within a wide and interdisciplinary scope. Superconductor Science and Technology is now publishing a selection of papers from the workshop which have been selected for their high quality. The selection comprises seven papers covering mathematical, physical and technological topics which contribute to an improvement in the development of procedures, understanding of phenomena and development of applications. We hope that they provide a perspective on the relevance and growth that the modelling of HTS superconductors has achieved in the past 25 years.

  4. Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection.

    Science.gov (United States)

    Wang, Yong; Wu, Qiao-Feng; Chen, Chen; Wu, Ling-Yun; Yan, Xian-Zhong; Yu, Shu-Guang; Zhang, Xiang-Sun; Liang, Fan-Rong

    2012-01-01

    Acupuncture has been practiced in China for thousands of years as part of the Traditional Chinese Medicine (TCM) and has gradually accepted in western countries as an alternative or complementary treatment. However, the underlying mechanism of acupuncture, especially whether there exists any difference between varies acupoints, remains largely unknown, which hinders its widespread use. In this study, we develop a novel Linear Programming based Feature Selection method (LPFS) to understand the mechanism of acupuncture effect, at molecular level, by revealing the metabolite biomarkers for acupuncture treatment. Specifically, we generate and investigate the high-throughput metabolic profiles of acupuncture treatment at several acupoints in human. To select the subsets of metabolites that best characterize the acupuncture effect for each meridian point, an optimization model is proposed to identify biomarkers from high-dimensional metabolic data from case and control samples. Importantly, we use nearest centroid as the prototype to simultaneously minimize the number of selected features and the leave-one-out cross validation error of classifier. We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR). We find that our LPFS method tends to reveal a small set of metabolites with small standard deviation and large shifts, which exactly serves our requirement for good biomarker. Biologically, several metabolite biomarkers for acupuncture treatment are revealed and serve as the candidates for further mechanism investigation. Also biomakers derived from five meridian points, Zusanli (ST36), Liangmen (ST21), Juliao (ST3), Yanglingquan (GB34), and Weizhong (BL40), are compared for their similarity and difference, which provide evidence for the specificity of acupoints. Our result demonstrates that metabolic profiling might be a promising method to

  5. THM-coupled modeling of selected processes in argillaceous rock relevant to rock mechanics

    International Nuclear Information System (INIS)

    Czaikowski, Oliver

    2012-01-01

    Scientific investigations in European countries other than Germany concentrate not only on granite formations (Switzerland, Sweden) but also on argillaceous rock formations (France, Switzerland, Belgium) to assess their suitability as host and barrier rock for the final storage of radioactive waste. In Germany, rock salt has been under thorough study as a host rock over the past few decades. According to a study by the German Federal Institute for Geosciences and Natural Resources, however, not only salt deposits but also argillaceous rock deposits are available at relevant depths and of extensions in space which make final storage of high-level radioactive waste basically possible in Germany. Equally qualified findings about the suitability/unsuitability of non-saline rock formations require fundamental studies to be conducted nationally because of the comparatively low level of knowledge. The article presents basic analyses of coupled mechanical and hydraulic properties of argillaceous rock formations as host rock for a repository. The interaction of various processes is explained on the basis of knowledge derived from laboratory studies, and open problems are deduced. For modeling coupled processes, a simplified analytical computation method is proposed and compared with the results of numerical simulations, and the limits to its application are outlined. (orig.)

  6. Strength and fatigability of selected muscles in upper limb: assessing muscle imbalance relevant to tennis elbow.

    Science.gov (United States)

    Alizadehkhaiyat, O; Fisher, A C; Kemp, G J; Frostick, S P

    2007-08-01

    The aetiology of tennis elbow has remained uncertain for more than a century. To examine muscle imbalance as a possible pathophysiological factor requires a reliable method of assessment. This paper describes the development of such a method and its performance in healthy subjects. We propose a combination of surface and fine-wire EMG of shoulder and forearm muscles and wrist strength measurements as a reliable tool for assessing muscle imbalance relevant to the pathophysiology of tennis elbow. Six healthy volunteers participated. EMG data were acquired at 50% maximal voluntary isometric contraction from five forearm muscles during grip and three shoulder muscles during external rotation and abduction, and analysed using normalized median frequency slope as a fatigue index. Wrist extension/flexion strength was measured using a purpose-built dynamometer. Significant negative slope of median frequency was found for all muscles, with good reproducibility, and no significant difference in slope between the different muscles of the shoulder and the wrist. (Amplitude slope showed high variability and was therefore unsuitable for this purpose.) Wrist flexion was 27+/-8% stronger than extension (mean+/-SEM, p=0.006). This is a reliable method for measuring muscle fatigue in forearm and shoulder. EMG and wrist strength studies together can be used for assessing and identifying the muscle balance in the wrist-forearm-shoulder chain.

  7. Exploring internal features of 16S rRNA gene for identification of clinically relevant species of the genus Streptococcus

    Science.gov (United States)

    2011-01-01

    Background Streptococcus is an economically important genus as a number of species belonging to this genus are human and animal pathogens. The genus has been divided into different groups based on 16S rRNA gene sequence similarity. The variability observed among the members of these groups is low and it is difficult to distinguish them. The present study was taken up to explore 16S rRNA gene sequence to develop methods that can be used for preliminary identification and can supplement the existing methods for identification of clinically-relevant isolates of the genus Streptococcus. Methods 16S rRNA gene sequences belonging to the isolates of S. dysgalactiae, S. equi, S. pyogenes, S. agalactiae, S. bovis, S. gallolyticus, S. mutans, S. sobrinus, S. mitis, S. pneumoniae, S. thermophilus and S. anginosus were analyzed with the purpose to define genetic variability within each species to generate a phylogenetic framework, to identify species-specific signatures and in-silico restriction enzyme analysis. Results The framework based analysis was used to segregate Streptococcus spp. previously identified upto genus level. This segregation was validated using species-specific signatures and in-silico restriction enzyme analysis. 43 uncharacterized Streptococcus spp. could be identified using this approach. Conclusions The markers generated exploring 16S rRNA gene sequences provided useful tool that can be further used for identification of different species of the genus Streptococcus. PMID:21702978

  8. Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Benjamin A Logsdon

    Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

  9. Supramolecular Chemistry of Selective Anion Recognition for Anions of Environmental Relevance. Final Report

    International Nuclear Information System (INIS)

    Bowman-James, Kristin

    2008-01-01

    This project focused on identifying selective binding agents for the removal of negative ions that hamper vitrification efforts. The project was led by Professor Kristin Bowman-James at the University of Kansas (KU) and consisted of a collaborative effort between the KU group, the group of Professor Jonathan Sessler at the University of Texas Austin (UT), and Dr. Bruce Moyer at Oak Ridge National Laboratory (ORNL). Research efforts during this funding period have focused on a particularly difficult target species, sulfate ion, which is highly insoluble in borosilicate glass. The project is unique in that it combines the synthetic expertise of the Bowman-James and Sessler groups with the analytical/extraction expertise of the Moyer group at ORNL, with a synergy that can bridge disciplines. Sulfate ion is a relatively small ionic species that, because of its solubility characteristics as well as its caustic nature, are a hindrance to the vitrification process. Not only is it relatively insoluble in the medium that has been chosen to store the wastes, but it also has the potential for accelerating the corrosion of the furnaces used to vitrify the borosilicate in a liquid to allow for waste incorporation. The goal of the Bowman-James group has been to design sequestering agents for sulfate that are selective for that ion over nitrate, for example, to allow for a liquid-liquid extraction step that can remove most of the sulfate prior to vitrification. The collaborative design efforts between the Bowman-James and Sessler groups helped to accelerate progress in the design process. In particular the Bowman-James group has been probing the influence of dimensionality and charge on sulfate binding, i.e., progressing from circular to spherical extractants, while at the same time adding positive charges to the agents so that when sulfate is bound, the resulting species is neutral. Not only does this project serve to benefit the remediation process, but it also provides an

  10. Selection pressure on human STR loci and its relevance in repeat expansion disease

    KAUST Repository

    Shimada, Makoto K.

    2016-06-11

    Short Tandem Repeats (STRs) comprise repeats of one to several base pairs. Because of the high mutability due to strand slippage during DNA synthesis, rapid evolutionary change in the number of repeating units directly shapes the range of repeat-number variation according to selection pressure. However, the remaining questions include: Why are STRs causing repeat expansion diseases maintained in the human population; and why are these limited to neurodegenerative diseases? By evaluating the genome-wide selection pressure on STRs using the database we constructed, we identified two different patterns of relationship in repeat-number polymorphisms between DNA and amino-acid sequences, although both patterns are evolutionary consequences of avoiding the formation of harmful long STRs. First, a mixture of degenerate codons is represented in poly-proline (poly-P) repeats. Second, long poly-glutamine (poly-Q) repeats are favored at the protein level; however, at the DNA level, STRs encoding long poly-Qs are frequently divided by synonymous SNPs. Furthermore, significant enrichments of apoptosis and neurodevelopment were biological processes found specifically in genes encoding poly-Qs with repeat polymorphism. This suggests the existence of a specific molecular function for polymorphic and/or long poly-Q stretches. Given that the poly-Qs causing expansion diseases were longer than other poly-Qs, even in healthy subjects, our results indicate that the evolutionary benefits of long and/or polymorphic poly-Q stretches outweigh the risks of long CAG repeats predisposing to pathological hyper-expansions. Molecular pathways in neurodevelopment requiring long and polymorphic poly-Q stretches may provide a clue to understanding why poly-Q expansion diseases are limited to neurodegenerative diseases. © 2016, Springer-Verlag Berlin Heidelberg.

  11. Influence of Feature Selection Methods on Classification Sensitivity Based on the Example of A Study of Polish Voivodship Tourist Attractiveness

    Directory of Open Access Journals (Sweden)

    Bąk Iwona

    2014-07-01

    Full Text Available The purpose of this article is to determine the influence of various methods of selection of diagnostic features on the sensitivity of classification. Three options of feature selection are presented: a parametric feature selection method with a sum (option I, a median of the correlation coefficients matrix column elements (option II and the method of a reversed matrix (option III. Efficiency of the groupings was verified by the indicators of homogeneity, heterogeneity and the correctness of grouping. In the assessment of group efficiency the approach with the Weber median was used. The undertaken problem was illustrated with a research into the tourist attractiveness of voivodships in Poland in 2011.

  12. Main Features of a 3d GIS for a Monumental Complex with AN Historical-Cultural Relevance

    Science.gov (United States)

    Scianna, A.; La Guardia, M.

    2017-05-01

    The last achievements of technologies in geomatics especially in survey and restitution of 3D models (UAV/drones and laser scanner technologies) generated new procedures and higher standards of quality in representation of archaeological sites. Together with Geomatics, the recent development of Information and Communication Technologies (ICT) strongly contribute to document and the Cultural Heritage (CH). The representation and documentation of CH using these new technologies has became necessary in order to satisfy different needs: - for restorers in order to acquire a deep knowledge of the cultural good and to define possible strategies of restoration; - for the conservation of information, allowing to preserve the 3D geometry of the monumental complex with the integration of descriptions about architectural elements; - for touristic aims, giving the opportunity of sharing CH information on web, allowing users to visit and explore, in a virtual way, monumental complexes, acquiring information details about architectural elements or the history of monumental complex. Looking through these new scenarios, the development of a 3D Geographic Information System (GIS) applied to a cultural good could be, today, an added value of fundamental importance for full description and data management of monumental complexes. In this work, the main features necessary for the correct construction of a 3D GIS of a monumental complex will be analyzed, with a particular focus on the possibilities for creating a standardized procedure to follow.

  13. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  14. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    International Nuclear Information System (INIS)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-01-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  15. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

    Science.gov (United States)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

  16. Co-Selection of Resistance to Antibiotics, Biocides and Heavy Metals, and Its Relevance to Foodborne Pathogens

    Directory of Open Access Journals (Sweden)

    Andrew D. Wales

    2015-11-01

    Full Text Available Concerns have been raised in recent years regarding co-selection for antibiotic resistance among bacteria exposed to biocides used as disinfectants, antiseptics and preservatives, and to heavy metals (particularly copper and zinc used as growth promoters and therapeutic agents for some livestock species. There is indeed experimental and observational evidence that exposure to these non-antibiotic antimicrobial agents can induce or select for bacterial adaptations that result in decreased susceptibility to one or more antibiotics. This may occur via cellular mechanisms that are protective across multiple classes of antimicrobial agents or by selection of genetic determinants for resistance to non-antibiotic agents that are linked to genes for antibiotic resistance. There may also be relevant effects of these antimicrobial agents on bacterial community structure and via non-specific mechanisms such as mobilization of genetic elements or mutagenesis. Notably, some co-selective adaptations have adverse effects on fitness in the absence of a continued selective pressure. The present review examines the evidence for the significance of these phenomena, particularly in respect of bacterial zoonotic agents that commonly occur in livestock and that may be transmitted, directly or via the food chain, to human populations.

  17. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

  18. Selective uptake and biological consequences of environmentally relevant antidepressant pharmaceutical exposures on male fathead minnows

    Science.gov (United States)

    Schultz, Melissa M.; Painter, Meghan M.; Bartell, Stephen E.; Logue, Amanda; Furlong, Edward T.; Werner, Stephen L.; Schoenfuss, Heiko L.

    2011-01-01

    Antidepressant pharmaceuticals have been reported in wastewater effluent at the nanogram to low microgram-per-liter range, and include bupropion (BUP), fluoxetine (FLX), sertraline (SER), and venlafaxine (VEN). To assess the effects of antidepressants on reproductive anatomy, physiology, and behavior, adult male fathead minnows (Pimeplwles promelas) were exposed for 21 days either to a single concentration of the antidepressants FLX, SER, VEN, or BUP, or to an antidepressant mixture. The data demonstrated that exposure to VEN (305 ng/L and 1104 ng/L) and SER (5.2 ng/L) resulted in mortality. Anatomical alterations were noted within the testes of fish exposed to SER and FLX, both modulators of the neurotransmitter serotonin. Additionally, FLX at 28 ng/L induced vitellogenin in male fish—a common endpoint for estrogenic endocrine disruption. Significant alterations in male secondary sex characteristics were noted with single exposures. Effects of single compound exposures neither carried over, nor became additive in the antidepressant mixtures, and reproductive behavior was not affected. Analysis of brain tissues from the exposed fish suggested increased uptake of FLX, SER and BUP and minimal uptake of VEN when compared to exposure water concentrations. Furthermore, the only metabolite detected consistently in the brain tissues was norfluoxetine. Similar trends of uptake by brain tissue were observed when fish were exposed to antidepressant mixtures. The present study demonstrates that anatomy and physiology, but not reproductive behavior, can be disrupted by exposure to environmental concentrations of some antidepressants. The observation that antidepressant uptake into fish tissues is selective may have consequences on assessing the mode-of-action and effects of these compounds in future studies.

  19. Using Impact-Relevant Sensitivities to Efficiently Evaluate and Select Climate Change Scenarios

    Science.gov (United States)

    Vano, J. A.; Kim, J. B.; Rupp, D. E.; Mote, P.

    2014-12-01

    We outline an efficient approach to help researchers and natural resource managers more effectively use global climate model information in their long-term planning. The approach provides an estimate of the magnitude of change of a particular impact (e.g., summertime streamflow) from a large ensemble of climate change projections prior to detailed analysis. These estimates provide both qualitative information as an end unto itself (e.g., the distribution of future changes between emissions scenarios for the specific impact) and a judicious, defensible evaluation structure that can be used to qualitatively select a sub-set of climate models for further analysis. More specifically, the evaluation identifies global climate model scenarios that both (1) span the range of possible futures for the variable/s most important to the impact under investigation, and (2) come from global climate models that adequately simulate historical climate, providing plausible results for the future climate in the region of interest. To identify how an ecosystem process responds to projected future changes, we methodically sample, using a simple sensitivity analysis, how an impact variable (e.g., streamflow magnitude, vegetation carbon) responds locally to projected regional temperature and precipitation changes. We demonstrate our technique over the Pacific Northwest, focusing on two types of impacts each in three distinct geographic settings: (a) changes in streamflow magnitudes in critical seasons for water management in the Willamette, Yakima, and Upper Columbia River basins; and (b) changes in annual vegetation carbon in the Oregon and Washington Coast Ranges, Western Cascades, and Columbia Basin ecoregions.

  20. Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

    Directory of Open Access Journals (Sweden)

    Tian Lan

    2007-01-01

    Full Text Available We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson at 2 difficulty levels (low/high demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.

  1. Features of application of test techniques in selection and certification of personnel

    Directory of Open Access Journals (Sweden)

    Levina E.V.

    2017-04-01

    Full Text Available this article investigates the problem of use of test techniques in the course of selection and certification of personnel, and also for detection of features of formation of social and psychological climate in small groups and labor collectives. The group dynamics including individual preferences of players in the choice of strategy of behavior in a conflict situation in definition of the most acceptable social role, in identification of the most constructive and effective type of the administrative decision has the special importance. The author of the article offers a set of the test methods allowing to define social and psychological type of the individual at a stage of staff recruitment and to reveal its individual behavioral preferences that can structurally influence on team work.

  2. Clustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes

    KAUST Repository

    Abusamra, Heba; Bajic, Vladimir B.

    2016-01-01

    decrease the computational time and cost, but also improve the classification performance. Among different approaches of feature selection methods, however most of them suffer from several problems such as lack of robustness, validation issues etc. Here, we

  3. Automatic Feature Selection and Weighting for the Formation of Homogeneous Groups for Regional Intensity-Duration-Frequency (IDF) Curve Estimation

    Science.gov (United States)

    Yang, Z.; Burn, D. H.

    2017-12-01

    Extreme rainfall events can have devastating impacts on society. To quantify the associated risk, the IDF curve has been used to provide the essential rainfall-related information for urban planning. However, the recent changes in the rainfall climatology caused by climate change and urbanization have made the estimates provided by the traditional regional IDF approach increasingly inaccurate. This inaccuracy is mainly caused by two problems: 1) The ineffective choice of similarity indicators for the formation of a homogeneous group at different regions; and 2) An inadequate number of stations in the pooling group that does not adequately reflect the optimal balance between group size and group homogeneity or achieve the lowest uncertainty in the rainfall quantiles estimates. For the first issue, to consider the temporal difference among different meteorological and topographic indicators, a three-layer design is proposed based on three stages in the extreme rainfall formation: cloud formation, rainfall generation and change of rainfall intensity above urban surface. During the process, the impacts from climate change and urbanization are considered through the inclusion of potential relevant features at each layer. Then to consider spatial difference of similarity indicators for the homogeneous group formation at various regions, an automatic feature selection and weighting algorithm, specifically the hybrid searching algorithm of Tabu search, Lagrange Multiplier and Fuzzy C-means Clustering, is used to select the optimal combination of features for the potential optimal homogenous groups formation at a specific region. For the second issue, to compare the uncertainty of rainfall quantile estimates among potential groups, the two sample Kolmogorov-Smirnov test-based sample ranking process is used. During the process, linear programming is used to rank these groups based on the confidence intervals of the quantile estimates. The proposed methodology fills the gap

  4. Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

    Directory of Open Access Journals (Sweden)

    Arpita Nagpal

    2017-01-01

    Full Text Available Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP. Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.

  5. The impact of feature selection on one and two-class classification performance for plant microRNAs.

    Science.gov (United States)

    Khalifa, Waleed; Yousef, Malik; Saçar Demirci, Müşerref Duygu; Allmer, Jens

    2016-01-01

    MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.

  6. The impact of feature selection on one and two-class classification performance for plant microRNAs

    Directory of Open Access Journals (Sweden)

    Waleed Khalifa

    2016-06-01

    Full Text Available MicroRNAs (miRNAs are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18–24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC is used in the field; because negative examples are hard to come by, one-class classification (OCC has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.

  7. Selective axonal growth of embryonic hippocampal neurons according to topographic features of various sizes and shapes

    Directory of Open Access Journals (Sweden)

    Christine E Schmidt

    2010-12-01

    Full Text Available David Y Fozdar1*, Jae Y Lee2*, Christine E Schmidt2–6, Shaochen Chen1,3–5,7,1Departments of Mechanical Engineering, 2Chemical Engineering, 3Biomedical Engineering; 4Center for Nano Molecular Science and Technology; 5Texas Materials Institute; 6Institute of Neuroscience; 7Microelectronics Research Center, The University of Texas at Austin, Austin, TX, USA *Contributed equally to this workPurpose: Understanding how surface features influence the establishment and outgrowth of the axon of developing neurons at the single cell level may aid in designing implantable scaffolds for the regeneration of damaged nerves. Past studies have shown that micropatterned ridge-groove structures not only instigate axon polarization, alignment, and extension, but are also preferred over smooth surfaces and even neurotrophic ligands.Methods: Here, we performed axonal-outgrowth competition assays using a proprietary four-quadrant topography grid to determine the capacity of various micropatterned topographies to act as stimuli sequestering axon extension. Each topography in the grid consisted of an array of microscale (approximately 2 µm or submicroscale (approximately 300 nm holes or lines with variable dimensions. Individual rat embryonic hippocampal cells were positioned either between two juxtaposing topographies or at the borders of individual topographies juxtaposing unpatterned smooth surface, cultured for 24 hours, and analyzed with respect to axonal selection using conventional imaging techniques.Results: Topography was found to influence axon formation and extension relative to smooth surface, and the distance of neurons relative to topography was found to impact whether the topography could serve as an effective cue. Neurons were also found to prefer submicroscale over microscale features and holes over lines for a given feature size.Conclusion: The results suggest that implementing physical cues of various shapes and sizes on nerve guidance conduits

  8. ARABIC TEXT CLASSIFICATION USING NEW STEMMER FOR FEATURE SELECTION AND DECISION TREES

    Directory of Open Access Journals (Sweden)

    SAID BAHASSINE

    2017-06-01

    Full Text Available Text classification is the process of assignment of unclassified text to appropriate classes based on their content. The most prevalent representation for text classification is the bag of words vector. In this representation, the words that appear in documents often have multiple morphological structures, grammatical forms. In most cases, this morphological variant of words belongs to the same category. In the first part of this paper, anew stemming algorithm was developed in which each term of a given document is represented by its root. In the second part, a comparative study is conducted of the impact of two stemming algorithms namely Khoja’s stemmer and our new stemmer (referred to hereafter by origin-stemmer on Arabic text classification. This investigation was carried out using chi-square as a feature of selection to reduce the dimensionality of the feature space and decision tree classifier. In order to evaluate the performance of the classifier, this study used a corpus that consists of 5070 documents independently classified into six categories: sport, entertainment, business, Middle East, switch and world on WEKA toolkit. The recall, f-measure and precision measures are used to compare the performance of the obtained models. The experimental results show that text classification using rout stemmer outperforms classification using Khoja’s stemmer. The f-measure was 92.9% in sport category and 89.1% in business category.

  9. Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

    Science.gov (United States)

    Mustaqeem, Anam; Anwar, Syed Muhammad; Majid, Muahammad

    2018-01-01

    Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

  10. Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

    Science.gov (United States)

    Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

    2014-09-24

    This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

  11. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Directory of Open Access Journals (Sweden)

    Nicole A Capela

    Full Text Available Human activity recognition (HAR, using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter. The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree. Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  12. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Science.gov (United States)

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    2015-01-01

    Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  13. Oregon OCS seafloor mapping: Selected lease blocks relevant to renewable energy

    Science.gov (United States)

    Cochrane, Guy R.; Hemery, Lenaïg G.; Henkel, Sarah K.

    2017-05-23

    publication is published in Cochrane (2017). All the data is provided as geographic information system (GIS) files that contain both Esri ArcGIS geotiffs or shapefiles. For those who do not own the full suite of Esri GIS and mapping software, the data can be read using Esri ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed August 29, 2016). Web services, which consist of standard implementations of ArcGIS representational state transfer (REST) Service and Open Geospatial Consortium (OGC) GIS web map service (WMS), also are available for all published GIS data. Web services were created using an ArcGIS service definition file, resulting in data layers that are symbolized as shown on the associated report figures. Both the ArcGIS REST Service and OGC WMS Service include all the individual GIS layers. Data layers are bundled together in a map-area web service; however, each layer can be symbolized and accessed individually after the web service is ingested into a desktop application or web map. Web services enable users to download and view data, as well as to easily add data to their own workflows, using any browser-enabled, standalone or mobile device.Though the surficial substrate is dominated by combinations of mud and sand substrate, a diverse assortment of geomorphologic features are related to geologic processes—one anticlinal ridge where bedrock is exposed, a slump and associated scarps, and pockmarks. Pockmarks are seen in the form of fields of small pockmarks, a lineation of large pockmarks with methanogenic carbonates, and areas of large pockmarks that have merged into larger variously shaped depressions. The slump appears to have originated at the pockmark lineation. Video-supervised numerical analysis of the MBES backscatter intensity data and vector ruggedness derived from the MBES bathymetry data was used to produce a substrate model called a seafloor character raster for the study area. The

  14. Teaching the Sociology of Popular Music with the Help of Feature Films: A Selected and Annotated Videography.

    Science.gov (United States)

    Groce, Stephen B.

    1992-01-01

    Discusses the use of feature films for courses on popular culture and the sociology of popular music. Suggests that films can illustrate topics such as culture, social groups, deviant behavior, racism, and sexism. Lists a selection of Hollywood feature films with accompanying readings and students' evaluations. (DK)

  15. Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry

    Directory of Open Access Journals (Sweden)

    Cremer Gerald

    2011-01-01

    Full Text Available Abstract Background Falls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients. Method A population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types. Results The results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test. Conclusions This study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide

  16. FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Taegwon Jeong

    2011-05-01

    Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  17. FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

    Science.gov (United States)

    Lee, Chongdeuk; Jeong, Taegwon

    2011-01-01

    Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  18. Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

    Directory of Open Access Journals (Sweden)

    Shipra Suman

    2017-10-01

    Full Text Available Wireless capsule endoscopy (WCE is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error. Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands. The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising.

  19. Understanding the selection of core head design features to match precisely challenging well applications

    Energy Technology Data Exchange (ETDEWEB)

    Zambrana, Roberto; Sousa, J. Tadeu V. de; Antunes, Ricardo [Halliburton Servicos Ltda., Rio de Janeiro, RJ (Brazil)

    2008-07-01

    Reliable rock mechanical information is very important for optimum reservoir development. This information can help specialists to accurately estimate reserves, reservoir compaction, sand production, stress field orientation, etc. In all cases, the solutions to problems involving rock mechanics lead to significant cost savings. Consequently, it is important that the decisions be based on the most accurate information possible. For the describing rock mechanics, cores represent the major source of data and therefore should be of good quality. However, there are several well conditions that cause coring and core recovery to be difficult, for example: unconsolidated formations; laminated and fractured rocks; critical mud losses, etc. The problem becomes even worse in high-inclination wells with long horizontal sections. In such situations, the optimum selections of core heads become critical. This paper will discuss the most important design features that enable core heads to be matched precisely to various challenging applications. Cases histories will be used to illustrate the superior performance of selected core heads. They include coring in horizontal wells and in harsh well conditions with critical mud losses. (author)

  20. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    Directory of Open Access Journals (Sweden)

    Mariela Cerrada

    2015-09-01

    Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  1. A feature selection approach for identification of signature genes from SAGE data

    Directory of Open Access Journals (Sweden)

    Silva Paulo JS

    2007-05-01

    Full Text Available Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS or the recent Sequencing-By-Synthesis (SBS technique. Some of such genes identified by the proposed method may be useful to generate classifiers.

  2. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    International Nuclear Information System (INIS)

    Rattá, G.A.; Vega, J.; Murari, A.

    2012-01-01

    Highlights: ► A new signal selection methodology to improve disruption prediction is reported. ► The approach is based on Genetic Algorithms. ► An advanced predictor has been created with the new set of signals. ► The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called “Advanced Predictor Of Disruptions” (APODIS), developed for the “Joint European Torus” (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals’ parameters in order to maximize the performance of the predictor is reported. The approach is based on “Genetic Algorithms” (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  3. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    Energy Technology Data Exchange (ETDEWEB)

    Ratta, G.A., E-mail: garatta@gateme.unsj.edu.ar [GATEME, Facultad de Ingenieria, Universidad Nacional de San Juan, Avda. San Martin 1109 (O), 5400 San Juan (Argentina); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense, 40, 28040 Madrid (Spain); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)

    2012-09-15

    Highlights: Black-Right-Pointing-Pointer A new signal selection methodology to improve disruption prediction is reported. Black-Right-Pointing-Pointer The approach is based on Genetic Algorithms. Black-Right-Pointing-Pointer An advanced predictor has been created with the new set of signals. Black-Right-Pointing-Pointer The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called 'Advanced Predictor Of Disruptions' (APODIS), developed for the 'Joint European Torus' (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals' parameters in order to maximize the performance of the predictor is reported. The approach is based on 'Genetic Algorithms' (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  4. The Speed of Feature-Based Attention: Attentional Advantage Is Slow, but Selection Is Fast

    Science.gov (United States)

    Huang, Liqiang

    2010-01-01

    When paying attention to a feature (e.g., red), no attentional advantage is gained in perceiving items with this feature in very brief displays. Therefore, feature-based attention seems to be slow. In previous feature-based attention studies, attention has often been measured as the difference in performance in a secondary task. In our recent work…

  5. Laser-induced Breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

    International Nuclear Information System (INIS)

    Yang, Jianhong; Yi, Cancan; Xu, Jinwu; Ma, Xianghong

    2015-01-01

    A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine. - Highlights: • Both training and testing samples are considered for analytical lines selection. • The analytical lines are auto-selected based on the built-in characteristics of spectral lines. • The new method can achieve better prediction accuracy and modeling robustness. • Model predictions are given with confidence interval of probabilistic distribution

  6. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

    Directory of Open Access Journals (Sweden)

    Yeom, Ha-Neul

    2014-09-01

    Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

  7. PLS-based and regularization-based methods for the selection of relevant variables in non-targeted metabolomics data

    Directory of Open Access Journals (Sweden)

    Renata Bujak

    2016-07-01

    Full Text Available Non-targeted metabolomics constitutes a part of systems biology and aims to determine many metabolites in complex biological samples. Datasets obtained in non-targeted metabolomics studies are multivariate and high-dimensional due to the sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA without and with multiple testing correction as well as least absolute shrinkage and selection operator (LASSO were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction, selected 46 and 218 variables based on VIP criteria using Pareto and UV scaling, respectively. In the case of the PH study, 217 and 320 variables were selected based on VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built with multiple testing correction, selected 4 and 19 variables as statistically significant in terms of Pareto and UV scaling, respectively. For PH study, 14 and 18 variables were selected based on VIP criteria in terms of Pareto and UV scaling, respectively. Additionally, the concept and fundaments of the least absolute shrinkage and selection operator (LASSO with bootstrap procedure evaluating reproducibility of results, was demonstrated. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3% and 100%. However, apart from the popularity of PLS-DA and OPLS-DA methods in metabolomics, it should be highlighted that they do not control type I or type II error, but only arbitrarily establish a cut-off value for PLS-DA loadings

  8. Feature-size dependent selective edge enhancement of x-ray images

    International Nuclear Information System (INIS)

    Herman, S.

    1988-01-01

    Morphological filters are nonlinear signal transformations that operate on a picture directly in the space domain. Such filters are based on the theory of mathematical morphology previously formulated. The filt4er being presented here features a ''mask'' operator (called a ''structuring element'' in some of the literature) which is a function of the two spatial coordinates x and y. The two basic mathematical operations are called ''masked erosion'' and ''masked dilation''. In the case of masked erosion the mask is passed over the input image in a raster pattern. At each position of the mask, the pixel values under the mask are multiplied by the mask pixel values. Then the output pixel value, located at the center position of the mask,is set equal to the minimum of the product of the mask and input values. Similarity, for masked dilation, the output pixel value is the maximum of the product of the input and the mask pixel values. The two basic processes of dilation and erosion can be used to construct the next level of operations the ''positive sieve'' (also called ''opening'') and the ''negative sieve'' (''closing''). The positive sieve modifies the peaks in the image whereas the negative sieve works on image valleys. The positive sieve is implemented by passing the output of the masked erosion step through the masked dilation function. The negative sieve reverses this procedure, using a dilation followed by an erosion. Each such sifting operator is characterized by a ''hole size''. It will be shown that the choice of hole size will select the range of pixel detail sizes which are to be enhanced. The shape of the mask will govern the shape of the enhancement. Finally positive sifting is used to enhance positive-going (peak) features, whereas negative enhances the negative-going (valley) landmarks

  9. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Maolong Xi

    2016-01-01

    Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

  10. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

    Science.gov (United States)

    Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

    2016-01-01

    This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363

  11. Selecting Informative Features of the Helicopter and Aircraft Acoustic Signals in the Adaptive Autonomous Information Systems for Recognition

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2017-01-01

    Full Text Available The article forms the rationale for selecting the informative features of the helicopter and aircraft acoustic signals to solve a problem of their recognition and shows that the most informative ones are the counts of extrema in the energy spectra of the input signals, which represent non-centered random variables. An apparatus of the multiple initial regression coefficients was selected as a mathematical tool of research. The application of digital re-circulators with positive and negative feedbacks, which have the comb-like frequency characteristics, solves the problem of selecting informative features. A distinguishing feature of such an approach is easy agility of the comb frequency characteristics just through the agility of a delay value of digital signal in the feedback circuit. Adding an adaptation block to the selection block of the informative features enables us to ensure the invariance of used informative feature and counts of local extrema of the spectral power density to the airspeed of a helicopter. The paper gives reasons for the principle of adaptation and the structure of the adaptation block. To form the discriminator characteristics are used the cross-correlation statistical characteristics of the quadrature components of acoustic signal realizations, obtained by Hilbert transform. The paper proposes to provide signal recognition using a regression algorithm that allows handling the non-centered informative features and using a priori information about coefficients of initial regression of signal and noise.The simulation in Matlab Simulink has shown that selected informative features of signals in regressive processing of signal realizations of 0.5 s duration have good separability, and based on a set of 100 acoustic signal realizations in each class in full-scale conditions, has proved ensuring a correct recognition probability of 0.975, at least. The considered principles of informative features selection and adaptation can

  12. EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar Umar

    2013-01-01

    The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.

  13. EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar Umar; Ganesan, Pugalenthi; Kalies, Kai Uwe; Hartmann, Enno; Martinetz, Thomas M.

    2013-01-01

    The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan

  14. PACS administrators' and radiologists' perspective on the importance of features for PACS selection.

    Science.gov (United States)

    Joshi, Vivek; Narra, Vamsi R; Joshi, Kailash; Lee, Kyootai; Melson, David

    2014-08-01

    Picture archiving and communication systems (PACS) play a critical role in radiology. This paper presents the criteria important to PACS administrators for selecting a PACS. A set of criteria are identified and organized into an integrative hierarchical framework. Survey responses from 48 administrators are used to identify the relative weights of these criteria through an analytical hierarchy process. The five main dimensions for PACS selection in order of importance are system continuity and functionality, system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the subdimensions, the highest weights were assessed for security, backup, and continuity; tools for continuous performance monitoring; support for multispecialty images; and voice recognition/transcription. PACS administrators' preferences were generally in line with that of previously reported results for radiologists. Both groups assigned the highest priority to ensuring business continuity and preventing loss of data through features such as security, backup, downtime prevention, and tools for continuous PACS performance monitoring. PACS administrators' next high priorities were support for multispecialty images, image retrieval speeds from short-term and long-term storage, real-time monitoring, and architectural issues of compatibility and integration with other products. Thus, next to ensuring business continuity, administrators' focus was on issues that impact their ability to deliver services and support. On the other hand, radiologists gave high priorities to voice recognition, transcription, and reporting; structured reporting; and convenience and responsiveness in manipulation of images. Thus, radiologists' focus appears to be on issues that may impact their productivity, effort, and accuracy.

  15. Relevant parameters in the micro silica selection for the self-flowing ultra-low cement castables production

    International Nuclear Information System (INIS)

    Studart, A.R.; Pandolfelli, V.C.; Rodrigues, J.A.; Vendrasco, S.L.

    1997-01-01

    Self-flowing ultra-low cement castables typically contain a large fraction of the particles, usually fume silica, which increase their flowability and mechanical strength at low temperatures. Fume silicas available in the market differ mainly from their amount of impurities. It is assumed that the content of soluble alkali and free carbon containing in this raw-material affects strongly the processing of self-flowing castable. In this work high alumina castables with gap-sized particle size distribution were prepared to evaluate their flowability, workability and mechanical strength for each sort of fume silica studied. It was observed that the amount of impurities affects both deflocculation and setting time of the castables and their cold and hot mechanical strength. Considerations regarding the physical and chemical characteristics relevant for selecting fume silicas for the production of self-flowing castables are presented and discussed. (author)

  16. Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

    Science.gov (United States)

    He, Song-Bing; Ben Hu; Kuang, Zheng-Kun; Wang, Dong; Kong, De-Xin

    2016-11-01

    Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2B vs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2A vs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

  17. Identification of Biomarkers for Esophageal Squamous Cell Carcinoma Using Feature Selection and Decision Tree Methods

    Directory of Open Access Journals (Sweden)

    Chun-Wei Tung

    2013-01-01

    Full Text Available Esophageal squamous cell cancer (ESCC is one of the most common fatal human cancers. The identification of biomarkers for early detection could be a promising strategy to decrease mortality. Previous studies utilized microarray techniques to identify more than one hundred genes; however, it is desirable to identify a small set of biomarkers for clinical use. This study proposes a sequential forward feature selection algorithm to design decision tree models for discriminating ESCC from normal tissues. Two potential biomarkers of RUVBL1 and CNIH were identified and validated based on two public available microarray datasets. To test the discrimination ability of the two biomarkers, 17 pairs of expression profiles of ESCC and normal tissues from Taiwanese male patients were measured by using microarray techniques. The classification accuracies of the two biomarkers in all three datasets were higher than 90%. Interpretable decision tree models were constructed to analyze expression patterns of the two biomarkers. RUVBL1 was consistently overexpressed in all three datasets, although we found inconsistent CNIH expression possibly affected by the diverse major risk factors for ESCC across different areas.

  18. Selection of yeasts with multifunctional features for application as starters in natural black table olive processing.

    Science.gov (United States)

    Bonatsou, S; Benítez, A; Rodríguez-Gómez, F; Panagou, E Z; Arroyo-López, F N

    2015-04-01

    Yeasts are unicellular eukaryotic microorganisms with a great importance in the elaboration on many foods and beverages. In the last years, researches have focused their attention to determine the favourable effects that these microorganisms could provide to table olive processing. In this context, the present study assesses, at laboratory scale, the potential technological (resistance to salt, lipase, esterase and β-glucosidase activities) and probiotic (phytase activity, survival to gastric and pancreatic digestions) features of 12 yeast strains originally isolated from Greek natural black table olive fermentations. The multivariate classification analysis carried out with all information obtained (a total of 336 quantitative input data), revealed that the most promising strains (clearly discriminated from the rest of isolates) were Pichia guilliermondii Y16 (which showed overall the highest resistance to salt and simulated digestions) and Wickerhamomyces anomalus Y18 (with the overall highest technological enzymatic activities), while the rest of strains were grouped together in two clearly differentiated clusters. Thus, this work opens the possibility for the evaluation of these two selected yeasts as multifunctional starters, alone or in combination with lactic acid bacteria, in real table olive fermentations. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Inhibitory Control of Feature Selectivity in an Object Motion Sensitive Circuit of the Retina

    Directory of Open Access Journals (Sweden)

    Tahnbee Kim

    2017-05-01

    Full Text Available Object motion sensitive (OMS W3-retinal ganglion cells (W3-RGCs in mice respond to local movements in a visual scene but remain silent during self-generated global image motion. The excitatory inputs that drive responses of W3-RGCs to local motion were recently characterized, but which inhibitory neurons suppress W3-RGCs’ responses to global motion, how these neurons encode motion information, and how their connections are organized along the excitatory circuit axis remains unknown. Here, we find that a genetically identified amacrine cell (AC type, TH2-AC, exhibits fast responses to global motion and slow responses to local motion. Optogenetic stimulation shows that TH2-ACs provide strong GABAA receptor-mediated input to W3-RGCs but only weak input to upstream excitatory neurons. Cell-type-specific silencing reveals that temporally coded inhibition from TH2-ACs cancels W3-RGC spike responses to global but not local motion stimuli and, thus, controls the feature selectivity of OMS signals sent to the brain.

  20. Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data.

    Science.gov (United States)

    Shah, M; Marchand, M; Corbeil, J

    2012-01-01

    One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of the well-known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with a much smaller number of genes while giving competitive classification accuracy but also having tight risk guarantees on future performance, unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.

  1. Feature selection for anomaly–based network intrusion detection using cluster validity indices

    CSIR Research Space (South Africa)

    Naidoo, Tyrone

    2015-09-01

    Full Text Available data, which is rarely available in operational networks. It uses normalized cluster validity indices as an objective function that is optimized over the search space of candidate feature subsets via a genetic algorithm. Feature sets produced...

  2. Brain responses to language-relevant musical features in adolescent cochlear implant users before and after an intensive music training program

    DEFF Research Database (Denmark)

    Petersen, Bjørn; Weed, Ethan; Hansen, Mads

    Brain responses to language-relevant musical features in adolescent cochlear implant users before and after an intensive music training program Petersen B.1,2, Weed E.1,3, Hansen M.1,4, Sørensen S.D.3 , Sandmann P.5 , Vuust P.1,2 1Center of Functionally Integrative Neuroscience, Aarhus University......, rhythm and intensity). Difference waves for the rhythm deviant were analyzed in the time window between 300 and 320 ms. Separate mixed-model ANOVAs were performed for left and right fronto-central electrodes. Paired t-tests were used to analyze the behavioral data. Here we present preliminary analyses...... of ERP responses to the rhythm deviant stimuli and results from a behavioral rhythm discrimination test. For both left and right electrode sites we found a main effect of group, driven by higher mean amplitude in the NH group. There was no main effect of training. Left hemisphere sites showed...

  3. Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

    Science.gov (United States)

    Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

    Fatigued monetary bills adversely affect the daily operation of automated teller machines (ATMs). In order to make the classification of fatigued bills more efficient, the development of an automatic fatigued monetary bill classification method is desirable. We propose a new method by which to estimate the fatigue level of monetary bills from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves the estimation accuracy of the fatigue level of monetary bills by adding frequency domain information to the acoustic energy pattern. The experimental results with real monetary bill samples reveal the effectiveness of the proposed method.

  4. Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2015-11-01

    Full Text Available Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

  5. A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P

    2015-02-01

    The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Is the emotion recognition deficit associated with frontotemporal dementia caused by selective inattention to diagnostic facial features?

    Science.gov (United States)

    Oliver, Lindsay D; Virani, Karim; Finger, Elizabeth C; Mitchell, Derek G V

    2014-07-01

    Frontotemporal dementia (FTD) is a debilitating neurodegenerative disorder characterized by severely impaired social and emotional behaviour, including emotion recognition deficits. Though fear recognition impairments seen in particular neurological and developmental disorders can be ameliorated by reallocating attention to critical facial features, the possibility that similar benefits can be conferred to patients with FTD has yet to be explored. In the current study, we examined the impact of presenting distinct regions of the face (whole face, eyes-only, and eyes-removed) on the ability to recognize expressions of anger, fear, disgust, and happiness in 24 patients with FTD and 24 healthy controls. A recognition deficit was demonstrated across emotions by patients with FTD relative to controls. Crucially, removal of diagnostic facial features resulted in an appropriate decline in performance for both groups; furthermore, patients with FTD demonstrated a lack of disproportionate improvement in emotion recognition accuracy as a result of isolating critical facial features relative to controls. Thus, unlike some neurological and developmental disorders featuring amygdala dysfunction, the emotion recognition deficit observed in FTD is not likely driven by selective inattention to critical facial features. Patients with FTD also mislabelled negative facial expressions as happy more often than controls, providing further evidence for abnormalities in the representation of positive affect in FTD. This work suggests that the emotional expression recognition deficit associated with FTD is unlikely to be rectified by adjusting selective attention to diagnostic features, as has proven useful in other select disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

    Science.gov (United States)

    Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

    2016-01-01

    MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

  8. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

    Science.gov (United States)

    Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

    2014-12-01

    Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  9. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Directory of Open Access Journals (Sweden)

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  10. Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

    Science.gov (United States)

    Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

    2014-11-01

    Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

  11. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Science.gov (United States)

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  12. Feature selection and classification of MAQC-II breast cancer and multiple myeloma microarray gene expression data.

    Directory of Open Access Journals (Sweden)

    Qingzhong Liu

    Full Text Available Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA, which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE, Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS, Gradient based Leave-one-out Gene Selection (GLGS. To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and

  13. Strong selection on mandible and nest features in a carpenter bee that nests in two sympatric host plants

    OpenAIRE

    Flores-Prado, Luis; Pinto, Carlos F; Rojas, Alejandra; Fontúrbel, Francisco E

    2014-01-01

    Host plants are used by herbivorous insects as feeding or nesting resources. In wood-boring insects, host plants features may impose selective forces leading to phenotypic differentiation on traits related to nest construction. Carpenter bees build their nests in dead stems or dry twigs of shrubs and trees; thus, mandibles are essential for the nesting process, and the nest is required for egg laying and offspring survival. We explored the shape and intensity of natural selection on phenotypi...

  14. Behavioral performance follows the time course of neural facilitation and suppression during cued shifts of feature-selective attention

    OpenAIRE

    Andersen, S. K.; Müller, M. M.

    2010-01-01

    A central question in the field of attention is whether visual processing is a strictly limited resource, which must be allocated by selective attention. If this were the case, attentional enhancement of one stimulus should invariably lead to suppression of unattended distracter stimuli. Here we examine voluntary cued shifts of feature-selective attention to either one of two superimposed red or blue random dot kinematograms (RDKs) to test whether such a reciprocal relationship between enhanc...

  15. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

    Science.gov (United States)

    2013-01-01

    Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313

  16. Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

    Science.gov (United States)

    Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

    2017-09-15

    Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number