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Sample records for machine svm classifier

  1. Image Reconstruction Using Multi Layer Perceptron MLP And Support Vector Machine SVM Classifier And Study Of Classification Accuracy

    Directory of Open Access Journals (Sweden)

    Shovasis Kumar Biswas

    2015-02-01

    Full Text Available Abstract Support Vector Machine SVM and back-propagation neural network BPNN has been applied successfully in many areas for example rule extraction classification and evaluation. In this paper we studied the back-propagation algorithm for training the multilayer artificial neural network and a support vector machine for data classification and image reconstruction aspects. A model focused on SVM with Gaussian RBF kernel is utilized here for data classification. Back propagation neural network is viewed as one of the most straightforward and is most general methods used for supervised training of multilayered neural network. We compared a support vector machine SVM with a back-propagation neural network BPNN for the task of data classification and image reconstruction. We made a comparison between the performances of the multi-class classification of these two learning methods. Comparing with these two methods we can conclude that the classification accuracy of the support vector machine is better and algorithm is much faster than the MLP with back propagation algorithm.

  2. Optimization of Support Vector Machine (SVM) for Object Classification

    Science.gov (United States)

    Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

    2012-01-01

    The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

  3. Application of SVM classifier in thermographic image classification for early detection of breast cancer

    Science.gov (United States)

    Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

    2016-09-01

    This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

  4. Reconfiguration-based implementation of SVM classifier on FPGA for Classifying Microarray data.

    Science.gov (United States)

    Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin

    2013-01-01

    Classifying Microarray data, which are of high dimensional nature, requires high computational power. Support Vector Machines-based classifier (SVM) is among the most common and successful classifiers used in the analysis of Microarray data but also requires high computational power due to its complex mathematical architecture. Implementing SVM on hardware exploits the parallelism available within the algorithm kernels to accelerate the classification of Microarray data. In this work, a flexible, dynamically and partially reconfigurable implementation of the SVM classifier on Field Programmable Gate Array (FPGA) is presented. The SVM architecture achieved up to 85× speed-up over equivalent general purpose processor (GPP) showing the capability of FPGAs in enhancing the performance of SVM-based analysis of Microarray data as well as future bioinformatics applications.

  5. COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

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    M. J. Baheti

    2012-01-01

    Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

  6. Efficient iris recognition via ICA feature and SVM classifier

    Institute of Scientific and Technical Information of China (English)

    Wang Yong; Xu Luping

    2007-01-01

    To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate, an iris recognition approach for combining SVM with ICA feature extraction model is presented. SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem. And ICA is a feature extraction technique which can be considered a generalization of principal component analysis. In this paper, ICA is used to generate a set of subsequences of feature vectors for iris feature extraction. Then each subsequence is classified using support vector machine sequence kernels. Experiments are made on CASIA iris database, the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.

  7. Face Detection Using Adaboosted SVM-Based Component Classifier

    CERN Document Server

    Valiollahzadeh, Seyyed Majid; Nazari, Mohammad

    2008-01-01

    Recently, Adaboost has been widely used to improve the accuracy of any given learning algorithm. In this paper we focus on designing an algorithm to employ combination of Adaboost with Support Vector Machine as weak component classifiers to be used in Face Detection Task. To obtain a set of effective SVM-weaklearner Classifier, this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one. Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of classification accuracy, to other commonly used Adaboost methods, such as Decision Trees and Neural Networks, on CMU+MIT face database. Results indicate that the performance of the proposed method is overall superior to previous Adaboost approaches.

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

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    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.

  9. SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

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    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. PMID:25295306

  10. Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles

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    Liliya Demidova

    2016-05-01

    Full Text Available The problem with development of the support vector machine (SVM classifiers using modified particle swarm optimization (PSO algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

  11. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

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    Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

    2015-01-01

    Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  12. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2015-01-01

    Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  13. An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

    Directory of Open Access Journals (Sweden)

    Ruben Ruiz-Gonzalez

    2014-11-01

    Full Text Available The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

  14. Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

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    Huiyan Jiang

    2015-01-01

    Full Text Available A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.

  15. Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA.

    Science.gov (United States)

    Jiang, Huiyan; Zhao, Di; Zheng, Ruiping; Ma, Xiaoqi

    2015-01-01

    A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.

  16. A linear-RBF multikernel SVM to classify big text corpora.

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    Romero, R; Iglesias, E L; Borrajo, L

    2015-01-01

    Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

  17. Image Reconstruction Using Pixel Wise Support Vector Machine SVM Classification.

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    Mohammad Mahmudul Alam Mia

    2015-02-01

    Full Text Available Abstract Image reconstruction using support vector machine SVM has been one of the major parts of image processing. The exactness of a supervised image classification is a function of the training data used in its generation. In this paper we studied support vector machine for classification aspects and reconstructed an image using support vector machine. Firstly value of the random pixels is used as the SVM classifier. Then the SVM classifier is trained by using those values of the random pixels. Finally the image is reconstructed after cross-validation with the trained SVM classifier. Matlab result shows that training with support vector machine produce better results and great computational efficiency with only a few minutes of runtime is necessary for training. Support vector machine have high classification accuracy and much faster convergence. Overall classification accuracy is 99.5. From our experiment It can be seen that classification accuracy mostly depends on the choice of the kernel function and best estimation of parameters for kernel is critical for a given image.

  18. MAMMOGRAMS ANALYSIS USING SVM CLASSIFIER IN COMBINED TRANSFORMS DOMAIN

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    B.N. Prathibha

    2011-02-01

    Full Text Available Breast cancer is a primary cause of mortality and morbidity in women. Reports reveal that earlier the detection of abnormalities, better the improvement in survival. Digital mammograms are one of the most effective means for detecting possible breast anomalies at early stages. Digital mammograms supported with Computer Aided Diagnostic (CAD systems help the radiologists in taking reliable decisions. The proposed CAD system extracts wavelet features and spectral features for the better classification of mammograms. The Support Vector Machines classifier is used to analyze 206 mammogram images from Mias database pertaining to the severity of abnormality, i.e., benign and malign. The proposed system gives 93.14% accuracy for discrimination between normal-malign and 87.25% accuracy for normal-benign samples and 89.22% accuracy for benign-malign samples. The study reveals that features extracted in hybrid transform domain with SVM classifier proves to be a promising tool for analysis of mammograms.

  19. COMBINING FEATURE SCALING ESTIMATION WITH SVM CLASSIFIER DESIGN USING GA APPROACH

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients of various features to the classification task, measured by real-valued scaling, are estimated efficiently by using GA. And GA exploits heavy-bias operator to promote sparsity in the scaling of features. There are many potential benefits of this method:Feature selection is performed by eliminating irrelevant features whose scaling is zero, an SVM classifier that has enhanced generalization ability can be learned simultaneously. Experimental comparisons using original SVM and GA-SVM demonstrate both economical feature selection and excellent classification accuracy on junk e-mail recognition problem and Internet ad recognition problem. The experimental results show that comparing with original SVM classifier, the number of support vector decreases significantly and better classification results are achieved based on GA-SVM. It also demonstrates that GA can provide a simple, general, and powerful framework for tuning parameters in optimal problem, which directly improves the recognition performance and recognition rate of SVM.

  20. Fiducial Points Detection Using SVM Linear Classifiers

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    Luiz Eduardo S. e Silva

    2014-04-01

    Full Text Available Currently, there is a growing interest from the sci entific and/or industrial community in respect to methods that offer solutions to the problem of fi ducial points detection in human faces. Some methods use the SVM for classification, but we obser ved that some formulations of optimization problems were not discussed. In this article, we pr opose to investigate the performance of mathematical formulation C-SVC when applied in fiduc ial point detection system. Futhermore, we explore new parameters for training the proposed system. The performance of the proposed system is evaluated in a fiducial points detection p roblem. The results demonstrate that the method is competitive

  1. The Entire Quantile Path of a Risk-Agnostic SVM Classifier

    CERN Document Server

    Yu, Jin; Zhang, Jian

    2012-01-01

    A quantile binary classifier uses the rule: Classify x as +1 if P(Y = 1|X = x) >= t, and as -1 otherwise, for a fixed quantile parameter t {[0, 1]. It has been shown that Support Vector Machines (SVMs) in the limit are quantile classifiers with t = 1/2 . In this paper, we show that by using asymmetric cost of misclassification SVMs can be appropriately extended to recover, in the limit, the quantile binary classifier for any t. We then present a principled algorithm to solve the extended SVM classifier for all values of t simultaneously. This has two implications: First, one can recover the entire conditional distribution P(Y = 1|X = x) = t for t {[0, 1]. Second, we can build a risk-agnostic SVM classifier where the cost of misclassification need not be known apriori. Preliminary numerical experiments show the effectiveness of the proposed algorithm.

  2. Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values.

    Science.gov (United States)

    Huang, Xiaolin; Shi, Lei; Suykens, Johan A K

    2016-04-08

    Applying the pinball loss in a support vector machine (SVM) classifier results in pin-SVM. The pinball loss is characterized by a parameter τ. Its value is related to the quantile level and different τ values are suitable for different problems. In this paper, we establish an algorithm to find the entire solution path for pin-SVM with different τ values. This algorithm is based on the fact that the optimal solution to pin-SVM is continuous and piecewise linear with respect to τ. We also show that the nonnegativity constraint on τ is not necessary, i.e., τ can be extended to negative values. First, in some applications, a negative τ leads to better accuracy. Second, τ = -1 corresponds to a simple solution that links SVM and the classical kernel rule. The solution for τ = -1 can be obtained directly and then be used as a starting point of the solution path. The proposed method efficiently traverses τ values through the solution path, and then achieves good performance by a suitable τ. In particular, $τ = 0$ corresponds to C-SVM, meaning that the traversal algorithm can output a result at least as good as C-SVM with respect to validation error.

  3. GenSVM: a generalized multiclass support vector machine

    NARCIS (Netherlands)

    G.J.J. van den Burg (Gerrit); P.J.F. Groenen (Patrick)

    2016-01-01

    textabstractTraditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM is proposed called GenSVM. In this method classification boundaries for a K-class proble

  4. Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

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    Sanal Kumar, K. P.; Bhavani, R., Dr.

    2017-08-01

    Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

  5. Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier

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    Jianming Zhang

    2015-01-01

    Full Text Available The position of the hinge point of mitral annulus (MA is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM classifier. The innovations are as follows: (1 designing a local context feature for MA in cardiac ultrasound image; (2 applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3 designing a weighted density field of candidates which represents the blocks of candidates; and (4 estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.

  6. Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier.

    Science.gov (United States)

    Zhang, Jianming; Liu, Yangchun; Xu, Wei

    2015-01-01

    The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM) classifier. The innovations are as follows: (1) designing a local context feature for MA in cardiac ultrasound image; (2) applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3) designing a weighted density field of candidates which represents the blocks of candidates; and (4) estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.

  7. Parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL

    Science.gov (United States)

    Madroñal, D.; Fabelo, H.; Lazcano, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

    2016-10-01

    Hyperspectral Imaging (HI) collects high resolution spectral information consisting of hundreds of bands across the electromagnetic spectrum -from the ultraviolet to the infrared range-. Thanks to this huge amount of information, an identification of the different elements that compound the hyperspectral image is feasible. Initially, HI was developed for remote sensing applications and, nowadays, its use has been spread to research fields such as security and medicine. In all of them, new applications that demand the specific requirement of real-time processing have appear. In order to fulfill this requirement, the intrinsic parallelism of the algorithms needs to be explicitly exploited. In this paper, a Support Vector Machine (SVM) classifier with a linear kernel has been implemented using a dataflow language called RVC-CAL. Specifically, RVC-CAL allows the scheduling of functional actors onto the target platform cores. Once the parallelism of the classifier has been extracted, a comparison of the SVM classifier implementation using LibSVM -a specific library for SVM applications- and RVC-CAL has been performed. The speedup results obtained for the image classifier depends on the number of blocks in which the image is divided; concretely, when 3 image blocks are processed in parallel, an average speed up above 2.50, with regard to the RVC-CAL sequential version, is achieved.

  8. Low rank updated LS-SVM classifiers for fast variable selection.

    Science.gov (United States)

    Ojeda, Fabian; Suykens, Johan A K; De Moor, Bart

    2008-01-01

    Least squares support vector machine (LS-SVM) classifiers are a class of kernel methods whose solution follows from a set of linear equations. In this work we present low rank modifications to the LS-SVM classifiers that are useful for fast and efficient variable selection. The inclusion or removal of a candidate variable can be represented as a low rank modification to the kernel matrix (linear kernel) of the LS-SVM classifier. In this way, the LS-SVM solution can be updated rather than being recomputed, which improves the efficiency of the overall variable selection process. Relevant variables are selected according to a closed form of the leave-one-out (LOO) error estimator, which is obtained as a by-product of the low rank modifications. The proposed approach is applied to several benchmark data sets as well as two microarray data sets. When compared to other related algorithms used for variable selection, simulations applying our approach clearly show a lower computational complexity together with good stability on the generalization error.

  9. Wavelet-SVM classifier based on texture features for land cover classification

    Science.gov (United States)

    Zhang, Ning; Wu, Bingfang; Zhu, Jianjun; Zhou, Yuemin; Zhu, Liang

    2008-12-01

    Texture features are recognized to be a special hint in images, which represent the spatial relations of the gray pixels. Nowadays, the applications of the texture analysis in image classification spread abroad. Combined with wavelet multi-resolution analysis or support vector machine statistical learning theory, texture analysis could improve the quality of classification increasingly. In this paper, we focus on the land cover for the Three Gorges reservoir using remote sensing data SPOT-5, a new classification method, wavelet-SVM classifier based on texture features, is employed for this study. Compare to the traditional maximum likelihood classifier and SVM classifier only use spectrum feature, this method produces more accurate classification results. According to the real environment of the Three Gorges reservoir land cover, a best texture group is selected from several texture features. Decompose the image at different levels, which is one of the main advantage of wavelet, and then compute the texture features in every sub-image, and the next step is eliminating the redundant, every texture features are centralized on the first principal components using principal component analysis. Finally, with the first principal components inputted, we can get the classification result using SVM in every decomposition scale, but what the problem we couldn't overlook is how to select the best SVM parameters. So an iterative rule based on the classification accuracy is induced, the more accuracy, the proper parameters.

  10. Multi-view L2-SVM and its multi-view core vector machine.

    Science.gov (United States)

    Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

    2016-03-01

    In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets.

  11. Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

    Institute of Scientific and Technical Information of China (English)

    张燕昆; 刘重庆

    2003-01-01

    Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an algorithm by combining SVM classifier with NNC to improve the correct recognition rate. We conduct the experiment on the Cambridge ORL face database. The result shows that our approach outperforms the standard eigenface approach and some other approaches.

  12. AN IMPLEMENTATION OF EIS-SVM CLASSIFIER USING RESEARCH ARTICLES FOR TEXT CLASSIFICATION

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    B Ramesh

    2016-04-01

    Full Text Available Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find root word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.

  13. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

    Science.gov (United States)

    Li, Qiang; Gu, Yu; Jia, Jing

    2017-01-30

    Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  14. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-01-01

    Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  15. Early Detection of Breast Cancer using SVM Classifier Technique

    CERN Document Server

    Rejani, Y Ireaneus Anna

    2009-01-01

    This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of (a) mammogram enhancement. (b) The segmentation of the tumor area. (c) The extraction of features from the segmented tumor area. (d) The use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include,...

  16. Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier

    Directory of Open Access Journals (Sweden)

    Kevin O'Neill

    2010-01-01

    Full Text Available The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP method to locate targets, the normalized surface magnetic source (NSMS model to characterize them, and a support vector machine (SVM to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

  17. Pulmonary nodule detection using a cascaded SVM classifier

    Science.gov (United States)

    Bergtholdt, Martin; Wiemker, Rafael; Klinder, Tobias

    2016-03-01

    Automatic detection of lung nodules from chest CT has been researched intensively over the last decades resulting also in several commercial products. However, solutions are adopted only slowly into daily clinical routine as many current CAD systems still potentially miss true nodules while at the same time generating too many false positives (FP). While many earlier approaches had to rely on rather few cases for development, larger databases become now available and can be used for algorithmic development. In this paper, we address the problem of lung nodule detection via a cascaded SVM classifier. The idea is to sequentially perform two classification tasks in order to select from an extremely large pool of potential candidates the few most likely ones. As the initial pool is allowed to contain thousands of candidates, very loose criteria could be applied during this pre-selection. In this way, the chances that a true nodule is falsely rejected as a candidate are reduced significantly. The final algorithm is trained and tested on the full LIDC/IDRI database. Comparison is done against two previously published CAD systems. Overall, the algorithm achieved sensitivity of 0.859 at 2.5 FP/volume where the other two achieved sensitivity values of 0.321 and 0.625, respectively. On low dose data sets, only slight increase in the number of FP/volume was observed, while the sensitivity was not affected.

  18. Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction.

    Science.gov (United States)

    Rahman, Julia; Mondal, Md Nazrul Islam; Islam, Md Khaled Ben; Hasan, Md Al Mehedi

    2016-12-18

    For the importance of protein subcellular localization in different branches of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays a most vital role in protein subcellular localization prediction specially in case of machine learning techniques. Single feature representation-like pseudo amino acid composition (PseAAC), physiochemical property models (PPM), and amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problems, we have proposed two feature fusion representations, AAIDPAAC and PPMPAAC, to work with Support Vector Machine classifiers, which fused PseAAC with PPM and AAID accordingly. We have evaluated the performance for both single and fused feature representation of a Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.

  19. Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.

    Science.gov (United States)

    Abdulkadir, Ahmed; Mortamet, Bénédicte; Vemuri, Prashanthi; Jack, Clifford R; Krueger, Gunnar; Klöppel, Stefan

    2011-10-01

    Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of

  20. SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

    Directory of Open Access Journals (Sweden)

    Demidova Liliya

    2017-01-01

    Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

  1. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

    Science.gov (United States)

    Khan, Saranjam; Ullah, Rahat; Khan, Asifullah; Wahab, Noorul; Bilal, Muhammad; Ahmed, Mushtaq

    2016-06-01

    The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

  2. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    Science.gov (United States)

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  3. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

    Directory of Open Access Journals (Sweden)

    Xu Huilei

    2010-12-01

    Full Text Available Abstract Background Mouse embryonic stem cells (mESCs are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership. Results For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG using support vector machines (SVM. The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier. Conclusions Our results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high

  4. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods.

  5. Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

    Science.gov (United States)

    Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin

    2015-01-01

    Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

  6. SVM and SVM Ensembles in Breast Cancer Prediction

    Science.gov (United States)

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. PMID:28060807

  7. SVM and SVM Ensembles in Breast Cancer Prediction.

    Science.gov (United States)

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

  8. A Stock Market Prediction Method Based on Support Vector Machines (SVM and Independent Component Analysis (ICA

    Directory of Open Access Journals (Sweden)

    Hakob GRIGORYAN

    2016-08-01

    Full Text Available The research presented in this work focuses on financial time series prediction problem. The integrated prediction model based on support vector machines (SVM with independent component analysis (ICA (called SVM-ICA is proposed for stock market prediction. The presented approach first uses ICA technique to extract important features from the research data, and then applies SVM technique to perform time series prediction. The results obtained from the SVM-ICA technique are compared with the results of SVM-based model without using any pre-processing step. In order to show the effectiveness of the proposed methodology, two different research data are used as illustrative examples. In experiments, the root mean square error (RMSE measure is used to evaluate the performance of proposed models. The comparative analysis leads to the conclusion that the proposed SVM-ICA model outperforms the simple SVM-based model in forecasting task of nonstationary time series.

  9. lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

    Science.gov (United States)

    Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

    2015-01-01

    Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

  10. 基于修正SVM-KNN组合算法的汉语专有名词自动抽取%Automatic Extraction on Chinese Proper Names Based on a Modified SVM-KNN Classifier

    Institute of Scientific and Technical Information of China (English)

    李丽双; 党延忠; 李丹

    2011-01-01

    Extracting Chinese proper names is a key step in the fields of text mining, information retrieval and machine translation.This paper presents a method of extracting proper names from Chinese texts based on the fusion of support vector machine (SVM) and modified K nearest neighbors (KNN).Different classifiers are used for classifying the different test samples in spatial distributions.In the class phase, the algorithm computes the distance from the test sample to the hyperplane of SVM.If the distance is greater than the given threshold, the test sample would be classified on SVM;otherwise, the KNN algorithm will be used.In the practical training corpora, the negative class is represented by a large number of examples while the positive one is represented by only a few.To fit the unbalanced data, a normalized KNN classifier is proposed to modify classic KNN.The experimental results show that this model is more efficient than sole SVM and classic SVM-KNN in extracting Chinese proper names.The modified SVM-KNN model can be generalized to other fields of machine learning with unbalanced class distribution.%专有名词的自动抽取是文本挖掘、信息检索和机器翻译等领域的关键技术.本文研究了组合SVM和KNN两种分类器进行汉语专有名词自动抽取的方法.对样本在空间的不同分布使用不同的分类方法,当测试样本与SVM最优超平面的距离大于给定的阈值时使用SVM分类,否则使用KNN;在实际训练语料中,常常是负类样本数远多于正类样本数,而传统KNN方法对不平衡训练集存在敏感性,所以提出了用归一化的思想对传统的KNN方法进行修正.实验表明,用SVM与修正的KNN组合算法进行汉语专有名词抽取比单一的SVM方法以及原始的SVM-KNN方法更具优越性,而且这种方法可以推广到其他非平衡分布样本的分类问题.

  11. SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

    NARCIS (Netherlands)

    P.J.F. Groenen (Patrick); G.I. Nalbantov (Georgi); J.C. Bioch (Cor)

    2007-01-01

    textabstractSupport vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal suppor

  12. Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

    Science.gov (United States)

    Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

    2010-01-01

    Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a

  13. Classifying Data Sets Using Support Vector Machines Based on Geometric Distance

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Support vector machines (SVMs) are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM (GDB-SVM). It takes the distance between a point and classified hyperplane as classification rule,and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C ++ 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all (OAA). Three predicted results of five of GDB-SVM are better than those of the method of one against one (OAO). Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO,but highly scalable for large data sets while generating high classification accuracy.

  14. Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.

    Science.gov (United States)

    Park, Eunjeong; Chang, Hyuk-Jae; Nam, Hyo Suk

    2017-04-18

    The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients. The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing. We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation. Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%. Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.

  15. 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.

  16. Classifying smoking urges via machine learning.

    Science.gov (United States)

    Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

    2016-12-01

    Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

  17. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

  18. Classification of skin cancer images using local binary pattern and SVM classifier

    Science.gov (United States)

    Adjed, Faouzi; Faye, Ibrahima; Ababsa, Fakhreddine; Gardezi, Syed Jamal; Dass, Sarat Chandra

    2016-11-01

    In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1% with sensitivity of 75.6% and specificity of 76.7%.

  19. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

    Science.gov (United States)

    Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  20. A new type SVM-projected SVM

    Institute of Scientific and Technical Information of China (English)

    ZHU; Yongsheng; ZHANG; Youyun

    2004-01-01

    Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.

  1. Segmentasi Citra menggunakan Support Vector Machine (SVM dan Ellipsoid Region Search Strategy (ERSS Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

    Directory of Open Access Journals (Sweden)

    Lukman Hakim

    2016-02-01

    . Firstly, the pixel-level color feature and texture feature of the image, which is used as input to SVM model (classifier, are extracted via the local homogeneity and Gray Level Co-Occurrence Matrix (GLCM. Then, determine class of classifier using Arimoto based ERSS thresholding. Finally, the color image is segmented with the trained SVM model (classifier. This image segmentation result less satisfied segmented image with 69 % accuracy. Feature reduction is needed to get an effective image segmentation. Key word: image segmentation, support vector machine, ERSS Arimoto Entropy, feature extraction.

  2. Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic (sEMG) signals. In contrast to the existing methods, considering the non-stationary and nonlinear characteristics of EMG signals, to get the more separable feature set, we introduce the empirical mode decomposition (EMD) to decompose the original EMG signals into several intrinsic mode functions (IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines (LS-SVMs), the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features, the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

  3. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Science.gov (United States)

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  4. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    Full Text Available In optical printed Chinese character recognition (OPCCR, many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  5. Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map

    Institute of Scientific and Technical Information of China (English)

    Nan Li; Li Jiang; Hong Liu; Hegao Cai; Dapeng Yang

    2012-01-01

    For controlling dexterous prosthetic hand with a high number of active Degrees of Freedom (DOF),it is necessary to reliably extract control volitions of finger motions from the human body.In this study,a large variety of finger motions are discriminated based on the diversities of the pressure distribution produced by the mechanical actions of muscles on the forearm.The pressure distribution patterns corresponding to the motions were measured by sensor array which is composed of 32 Force Sensitive Resistor (FSR) sensors.In order to map the pressure patterns with different finger motions,a multiclass classifier was designed based on the Support Vector Machine (SVM) algorithm.The multi-subject experiments show that it is possible to identify as many as seventeen different finger motions,including individual finger motions and multi-finger grasping motions,with the accuracy above 99% in the in-session validation.Further,the cross-session validation demonstrates that the performance of the proposed method is robust for use if the FSR array is not reset.The results suggest that the proposed method has great application prospects for the control of multi-DOF dexterous hand prosthesis.

  6. Tackling missing data in community health studies using additive LS-SVM classifier.

    Science.gov (United States)

    Wang, Guanjin; Deng, Zhaohong; Choi, Kup-Sze

    2016-12-01

    Missing data is a common issue in community health and epidemiological studies. Direct removal of samples with missing data can lead to reduced sample size and information bias, which deteriorates the significance of the results. While data imputation methods are available to deal with missing data, they are limited in performance and could introduce noises into the dataset. Instead of data imputation, a novel method based on additive least square support vector machine (LS-SVM) is proposed in this paper for predictive modeling when the input features of the model contain missing data. The method also determines simultaneously the influence of the features with missing values on the classification accuracy using the fast leave-one-out cross-validation strategy. The performance of the method is evaluated by applying it to predict the quality of life (QOL) of elderly people using health data collected in the community. The dataset involves demographics, socioeconomic status, health history and the outcomes of health assessments of 444 community-dwelling elderly people, with 5% to 60% of data missing in some of the input features. The QOL is measured using a standard questionnaire of the World Health Organization. Results show that the proposed method outperforms four conventional methods for handling missing data - case deletion, feature deletion, mean imputation and K-nearest neighbor imputation, with the average QOL prediction accuracy reaching 0.7418. It is potentially a promising technique for tackling missing data in community health research and other applications.

  7. Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

    Science.gov (United States)

    Zhang, Qigui; Deng, Kai

    2016-12-01

    As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

  8. Least Square Support Vector Machine Classifier vs a Logistic Regression Classifier on the Recognition of Numeric Digits

    Directory of Open Access Journals (Sweden)

    Danilo A. López-Sarmiento

    2013-11-01

    Full Text Available In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9 handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9, each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction. Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.

  9. Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM Method

    Directory of Open Access Journals (Sweden)

    Nian Zhang

    2014-08-01

    Full Text Available The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF kernel framework was built on the data set to optimize the tuning parameters and to obtain the moderated output. The training process of LS-SVM was designed to select both kernel parameters and regularization constants. The USGS real-time water data were used as time series input. 50% of the data were used for training, and 50% were used for testing. The experimental results showed that the LS-SVM algorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field.

  10. A Hybrid Fuzzy-SVM classifier for automated lung diseases diagnosis

    Science.gov (United States)

    Ben Hassen, Donia; Ben Zakour, Sihem; Taleb, Hassen

    2016-12-01

    A novel scheme for lesions classification in chest radiographs is presented in this paper. Features are extracted from detected lesions from lung regions which are segmented automatically. Then, we needed to eliminate redundant variables from the subset extracted because they affect the performance of the classification. We used Stepwise Forward Selection and Principal Components Analysis. Then, we obtained two subsets of features. We finally experimented the Stepwise/FCM/SVM classification and the PCA/FCM/SVM one. The ROC curves show that the hybrid PCA/FCM/SVM has relatively better accuracy and remarkable higher efficiency. Experimental results suggest that this approach may be helpful to radiologists for reading chest images.

  11. Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier

    Directory of Open Access Journals (Sweden)

    Wanlin Zhao

    2016-01-01

    Full Text Available The fault diagnosis of hydraulic pumps is currently important and significant to ensure the normal operation of the entire hydraulic system. Considering the nonlinear characteristics of hydraulic-pump vibration signals and the mode mixing problem of the original Empirical Mode Decomposition (EMD method, first, we use the Complete Ensemble EMD (CEEMD method to decompose the signals. Second, the time-frequency analysis methods, which include the Short-Time Fourier Transform (STFT and time-frequency entropy calculation, are applied to realize the robust feature extraction. Third, the multiclass Support Vector Machine (SVM classifier is introduced to automatically classify the fault mode in this paper. An actual hydraulic-pump experiment demonstrates the procedure with a complete feature extraction and accurate mode classification.

  12. Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

    Directory of Open Access Journals (Sweden)

    Ashley I. Heinson

    2017-02-01

    Full Text Available Reverse vaccinology (RV is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML techniques to distinguish bacterial protective antigens (BPAs from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM classifier that could discriminate BPAs (n = 200 from non-BPAs (n = 200 with an area under the curve (AUC of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

  13. Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

    Science.gov (United States)

    Heinson, Ashley I.; Gunawardana, Yawwani; Moesker, Bastiaan; Denman Hume, Carmen C.; Vataga, Elena; Hall, Yper; Stylianou, Elena; McShane, Helen; Williams, Ann; Niranjan, Mahesan; Woelk, Christopher H.

    2017-01-01

    Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future. PMID:28157153

  14. Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

    Science.gov (United States)

    Heinson, Ashley I; Gunawardana, Yawwani; Moesker, Bastiaan; Hume, Carmen C Denman; Vataga, Elena; Hall, Yper; Stylianou, Elena; McShane, Helen; Williams, Ann; Niranjan, Mahesan; Woelk, Christopher H

    2017-02-01

    Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

  15. Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

    KAUST Repository

    Heinson, Ashley

    2017-02-01

    Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

  16. A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2014-06-27

    Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

  17. Classifier transfer with data selection strategies for online support vector machine classification with class imbalance

    Science.gov (United States)

    Krell, Mario Michael; Wilshusen, Nils; Seeland, Anett; Kim, Su Kyoung

    2017-04-01

    Objective. Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM). Approach. We focus on data selection strategies which limit the size of the stored training data by different inclusion, exclusion, and further dataset manipulation criteria like handling class imbalance with two new approaches. We provide a comparison of the strategies with linear SVMs on several synthetic datasets with different data shifts as well as on different transfer settings with electroencephalographic (EEG) data. Main results. For the synthetic data, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add samples near the decision boundary of the SVM which reduces processing resources. Significance. For brain-computer interfaces based on EEG data, models trained on data from a calibration session, a previous recording session, or even from a recording session with another subject are used. We show, that by using the right combination of data selection criteria, it is possible to adapt the SVM classifier to overcome the performance drop from the transfer.

  18. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

    OpenAIRE

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user...

  19. TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification

    OpenAIRE

    Bresson, Xavier; Zhang, Ruiliang

    2012-01-01

    We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV classification perform significantly better when the number of labeled data is small.

  20. BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    M. Ustuner

    2016-06-01

    Full Text Available The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size, resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM, Maximum Likelihood (ML and Artificial Neural Network (ANN classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN while SVM is not affected significantly (from 94.38% to 94.69% and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

  1. Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

    Science.gov (United States)

    Ustuner, M.; Sanli, F. B.; Abdikan, S.

    2016-06-01

    The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

  2. A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

    Science.gov (United States)

    Mohan, Dhanya; Kumar, C. Santhosh

    2016-03-01

    Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

  3. Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier.

    Science.gov (United States)

    Al-Angari, Haitham M; Sahakian, Alan V

    2012-05-01

    Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.

  4. Efficient HIK SVM learning for image classification.

    Science.gov (United States)

    Wu, Jianxin

    2012-10-01

    Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

  5. F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

    OpenAIRE

    Wu, Xiaohe; Zuo, Wangmeng; ZHU, YUANYUAN; Lin, Liang

    2015-01-01

    The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of the transformation matrix to be diagonal, or are non-convex and computationally expensive. In this ...

  6. The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan

    Indian Academy of Sciences (India)

    Ramakrushna Reddy; Rajesh R Nair

    2013-10-01

    This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.

  7. SVM with discriminative dynamic time alignment

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In the past several years, support vector machines (SVM) have achieved a huge success in many field, especially in pattern recognition. But the standard SVM cannot deal with length-variable vectors, which is one severe obstacle for its applications to some important areas, such as speech recognition and part-of-speech tagging. The paper proposed a novel SVM with discriminative dynamic time alignment (DDTA-SVM) to solve this problem. When training DDTA-SVM classifier, according to the category information of the training Samples, different time alignment strategies were adopted to manipulate them in the kernel functions, which contributed to great improvement for training speed and generalization capability of the classifier. Since the alignment operator was embedded in kernel functions, the training algorithms of standard SVM were still compatible in DDTA-SVM. In order to increase the reliability of the classification, a new classification algorithm was suggested. The preliminary experimental results on Chinese confusable syllables speech classification task show that DDTA-SVM obtains faster convergence speed and better classification performance than dynamic time alignment kernel SVM (DTAK-SVM).Moreover, DDTA-SVM also gives higher classification precision compared to the conventional HMM. This proves that the proposed method is effective, especially for confusable lengthvariable pattern classification tasks.

  8. A Non-Destructive Method for Distinguishing Reindeer Antler (Rangifer tarandus) from Red Deer Antler (Cervus elaphus) Using X-Ray Micro-Tomography Coupled with SVM Classifiers

    Science.gov (United States)

    Lefebvre, Alexandre; Rochefort, Gael Y.; Santos, Frédéric; Le Denmat, Dominique; Salmon, Benjamin; Pétillon, Jean-Marc

    2016-01-01

    Over the last decade, biomedical 3D-imaging tools have gained widespread use in the analysis of prehistoric bone artefacts. While initial attempts to characterise the major categories used in osseous industry (i.e. bone, antler, and dentine/ivory) have been successful, the taxonomic determination of prehistoric artefacts remains to be investigated. The distinction between reindeer and red deer antler can be challenging, particularly in cases of anthropic and/or taphonomic modifications. In addition to the range of destructive physicochemical identification methods available (mass spectrometry, isotopic ratio, and DNA analysis), X-ray micro-tomography (micro-CT) provides convincing non-destructive 3D images and analyses. This paper presents the experimental protocol (sample scans, image processing, and statistical analysis) we have developed in order to identify modern and archaeological antler collections (from Isturitz, France). This original method is based on bone microstructure analysis combined with advanced statistical support vector machine (SVM) classifiers. A combination of six microarchitecture biomarkers (bone volume fraction, trabecular number, trabecular separation, trabecular thickness, trabecular bone pattern factor, and structure model index) were screened using micro-CT in order to characterise internal alveolar structure. Overall, reindeer alveoli presented a tighter mesh than red deer alveoli, and statistical analysis allowed us to distinguish archaeological antler by species with an accuracy of 96%, regardless of anatomical location on the antler. In conclusion, micro-CT combined with SVM classifiers proves to be a promising additional non-destructive method for antler identification, suitable for archaeological artefacts whose degree of human modification and cultural heritage or scientific value has previously made it impossible (tools, ornaments, etc.). PMID:26901355

  9. 支持向量机研究进展%Advances of Support Vector Machines(SVM)

    Institute of Scientific and Technical Information of China (English)

    顾亚祥; 丁世飞

    2011-01-01

    Support vector machines(SVM) are widespread attended for its excellent ability to learn, that are based on statistical learning theory. But in dealing with large-scale quadratic programming(QP) problem, traditional SVM will take too long time of training time, and has low efficiency and so on. This paper made a summarize of the new progress in the SVM training of algorithm,and made analysis and comparison on main algorithm,pointed out the advantages and disadvantages of them,focused on new progress in the current study — Fuzzy Support Vector Machine and Granular Support Vector Machine. Then the two mainly applications — classification and regression of SVM were discussed. Finally, the article gave the future research directions on SVM prediction.%基于统计学习理论的支持向量机(Support vector machines,SVM)以其优秀的学习能力受到广泛的关注.但传统支持向量机在处理大规模二次规划问题时会出现训练时间长、效率低下等问题.对SVM训练算法的最新研究成果进行了综述,对主要算法进行了比较深入的分析和比较,指出了各自的优点及其存在的问题,并且着重介绍了目前研究的新进展--模糊SVM和粒度SVM.接着论述了SVM主要的两方面应用--分类和回归.最后给出了今后SVM研究方向的预见.

  10. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines.

    Science.gov (United States)

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net.

  11. Support vector machine classifiers for large data sets.

    Energy Technology Data Exchange (ETDEWEB)

    Gertz, E. M.; Griffin, J. D.

    2006-01-31

    This report concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. Several methods are proposed based on interior point methods for convex quadratic programming. Software implementations are developed by adapting the object-oriented packaging OOQP to the problem structure and by using the software package PETSc to perform time-intensive computations in a distributed setting. Linear systems arising from classification problems with moderately large numbers of features are solved by using two techniques--one a parallel direct solver, the other a Krylov-subspace method incorporating novel preconditioning strategies. Numerical results are provided, and computational experience is discussed.

  12. Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

    Science.gov (United States)

    Matoug, S.; Abdel-Dayem, A.; Passi, K.; Gross, W.; Alqarni, M.

    2012-02-01

    Alzheimer's disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development of the disease, i.e., the conversion from prodromal stages (mild cognitive impairment) to Alzheimer's disease, which is the most critical brain disease for the senior population. Advanced medical imaging such as the volumetric MRI can detect changes in the size of brain regions due to the loss of the brain tissues. Measuring regions that atrophy during the progress of Alzheimer's disease can help neurologists in detecting and staging the disease. In the present investigation, we present a pseudo-automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs segmentation in order to detect the region of brain's ventricle, generates a feature vector that characterizes this region, creates an SQL database that contains the generated data, and finally classifies the images based on the extracted features. For our results, we have used the MRI data sets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

  13. Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery

    Science.gov (United States)

    Piragnolo, Marco; Masiero, Andrea; Pirotti, Francesco

    2017-04-01

    Since recent years surveying with unmanned aerial vehicles (UAV) is getting a great amount of attention due to decreasing costs, higher precision and flexibility of usage. UAVs have been applied for geomorphological investigations, forestry, precision agriculture, cultural heritage assessment and for archaeological purposes. It can be used for land use and land cover classification (LULC). In literature, there are two main types of approaches for classification of remote sensing imagery: pixel-based and object-based. On one hand, pixel-based approach mostly uses training areas to define classes and respective spectral signatures. On the other hand, object-based classification considers pixels, scale, spatial information and texture information for creating homogeneous objects. Machine learning methods have been applied successfully for classification, and their use is increasing due to the availability of faster computing capabilities. The methods learn and train the model from previous computation. Two machine learning methods which have given good results in previous investigations are Random Forest (RF) and Support Vector Machine (SVM). The goal of this work is to compare RF and SVM methods for classifying LULC using images collected with a fixed wing UAV. The processing chain regarding classification uses packages in R, an open source scripting language for data analysis, which provides all necessary algorithms. The imagery was acquired and processed in November 2015 with cameras providing information over the red, blue, green and near infrared wavelength reflectivity over a testing area in the campus of Agripolis, in Italy. Images were elaborated and ortho-rectified through Agisoft Photoscan. The ortho-rectified image is the full data set, and the test set is derived from partial sub-setting of the full data set. Different tests have been carried out, using a percentage from 2 % to 20 % of the total. Ten training sets and ten validation sets are obtained from

  14. Data on Support Vector Machines (SVM model to forecast photovoltaic power

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-12-01

    Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

  15. Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

    Science.gov (United States)

    Malvoni, M; De Giorgi, M G; Congedo, P M

    2016-12-01

    The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

  16. Classifiers in Japanese-to-English Machine Translation

    CERN Document Server

    Bond, F; Ikehara, S; Bond, Francis; Ogura, Kentaro; Ikehara, Satoru

    1996-01-01

    This paper proposes an analysis of classifiers into four major types: UNIT, METRIC, GROUP and SPECIES, based on properties of both Japanese and English. The analysis makes possible a uniform and straightforward treatment of noun phrases headed by classifiers in Japanese-to-English machine translation, and has been implemented in the MT system ALT-J/E. Although the analysis is based on the characteristics of, and differences between, Japanese and English, it is shown to be also applicable to the unrelated language Thai.

  17. Combination of the Manifold Dimensionality Reduction Methods with Least Squares Support vector machines for Classifying the Species of Sorghum Seeds

    Science.gov (United States)

    Chen, Y. M.; Lin, P.; He, J. Q.; He, Y.; Li, X. L.

    2016-01-01

    This study was carried out for rapid and noninvasive determination of the class of sorghum species by using the manifold dimensionality reduction (MDR) method and the nonlinear regression method of least squares support vector machines (LS-SVM) combing with the mid-infrared spectroscopy (MIRS) techniques. The methods of Durbin and Run test of augmented partial residual plot (APaRP) were performed to diagnose the nonlinearity of the raw spectral data. The nonlinear MDR methods of isometric feature mapping (ISOMAP), local linear embedding, laplacian eigenmaps and local tangent space alignment, as well as the linear MDR methods of principle component analysis and metric multidimensional scaling were employed to extract the feature variables. The extracted characteristic variables were utilized as the input of LS-SVM and established the relationship between the spectra and the target attributes. The mean average precision (MAP) scores and prediction accuracy were respectively used to evaluate the performance of models. The prediction results showed that the ISOMAP-LS-SVM model obtained the best classification performance, where the MAP scores and prediction accuracy were 0.947 and 92.86%, respectively. It can be concluded that the ISOMAP-LS-SVM model combined with the MIRS technique has the potential of classifying the species of sorghum in a reasonable accuracy.

  18. Neural Networks Classifier for Data Selection in Statistical Machine Translation

    OpenAIRE

    Peris, Álvaro; Chinea-Rios, Mara; Casacuberta, Francisco

    2016-01-01

    We address the data selection problem in statistical machine translation (SMT) as a classification task. The new data selection method is based on a neural network classifier. We present a new method description and empirical results proving that our data selection method provides better translation quality, compared to a state-of-the-art method (i.e., Cross entropy). Moreover, the empirical results reported are coherent across different language pairs.

  19. Defending Malicious Script Attacks Using Machine Learning Classifiers

    Directory of Open Access Journals (Sweden)

    Nayeem Khan

    2017-01-01

    Full Text Available The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

  20. Providing an efficient intelligent transportation system through detection, tracking and recognition of the region of interest in traffic signs by using non-linear SVM classifier in line with histogram oriented gradient and Kalman filter approach

    Indian Academy of Sciences (India)

    A H Mazinan; M Sarikhani

    2014-02-01

    With a focus on new researches in the area of intelligent transportation systems (ITS), an efficient approach has been investigated here. Based on the present view point, analysis of traffic signs are first considered via intelligence based approach, which is carried out through three main stages including detection, tracking and recognition, respectively, in this research. The key role of detection is to identify traffic signs by classification of road sign shapes in accordance with their signatures. This classification consists of four different shapes of circle, semicircle, triangle and square, as well. The linear classification of traffic sign is also carried out via support vector machine (SVM) by using one against all (OAA), since the present SVMs classifiers realized via linear kernel. The next step is to track traffic sign. It should be noted that this technique is now developed to reduce the searching mode in case of the whole area to be optimized its computational processing, consequently. This research work is investigated by realizing Kalman filter approach, where, finally, in recognition step, a feature of the region of interest (ROI) has been extracted for SVM classification. Histogram of oriented gradient (HOG) is realized in organizing the approach, as long as Gaussian kernel is also developed for non-linear SVM classifier.

  1. Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

    Science.gov (United States)

    Zhang, Xia; Amin, Elizabeth Ambrose

    2016-01-01

    Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets.

  2. Improved Approach Based on SVM for License Plate Character Recognition

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-hua; WANG Xiao-guang

    2005-01-01

    An improved approach based on support vector machine (SVM) called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extracted. A minimal set called the margin vector set, which contains all support vectors, is extracted. These margin vectors compose new training data and construct the classifier by using the general SVM optimized. The experimental results show that the improved SVM method does well at correct rate and training speed.

  3. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from {sup 18}F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Gao, Xuan [Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin (China); Chu, Chunyu [HIT–INSA Sino French Research Centre for Biomedical Imaging, Harbin Institute of Technology, Harbin (China); Li, Yingci; Lu, Peiou; Wang, Wenzhi [Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin (China); Liu, Wanyu [HIT–INSA Sino French Research Centre for Biomedical Imaging, Harbin Institute of Technology, Harbin (China); Yu, Lijuan, E-mail: yulijuan2003@126.com [Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin (China)

    2015-02-15

    Highlights: • Three support vector machine classifiers were constructed from PET-CT images. • The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively. • The areas under curves for maximum short diameter and SUV{sub max} were 0.684 and 0.652, respectively. • The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes. - Abstract: Objectives: In clinical practice, image analysis is dependent on simply visual perception and the diagnostic efficacy of this analysis pattern is limited for mediastinal lymph nodes in patients with lung cancer. In order to improve diagnostic efficacy, we developed a new computer-based algorithm and tested its diagnostic efficacy. Methods: 132 consecutive patients with lung cancer underwent {sup 18}F-FDG PET/CT examination before treatment. After all data were imported into the database of an on-line medical image analysis platform, the diagnostic efficacy of visual analysis was first evaluated without knowing pathological results, and the maximum short diameter and maximum standardized uptake value (SUV{sub max}) were measured. Then lymph nodes were segmented manually. Three classifiers based on support vector machine (SVM) were constructed from CT, PET, and combined PET-CT images, respectively. The diagnostic efficacy of SVM classifiers was obtained and evaluated. Results: According to ROC curves, the areas under curves for maximum short diameter and SUV{sub max} were 0.684 and 0.652, respectively. The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively. Conclusion: The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes.

  4. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

    Science.gov (United States)

    Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

    2014-08-01

    Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Classifying BCI signals from novice users with extreme learning machine

    Science.gov (United States)

    Rodríguez-Bermúdez, Germán; Bueno-Crespo, Andrés; José Martinez-Albaladejo, F.

    2017-07-01

    Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  6. Classifying BCI signals from novice users with extreme learning machine

    Directory of Open Access Journals (Sweden)

    Rodríguez-Bermúdez Germán

    2017-07-01

    Full Text Available Brain computer interface (BCI allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  7. Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

    Directory of Open Access Journals (Sweden)

    Nurul Arneida Husin

    2012-04-01

    Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

  8. Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

    Directory of Open Access Journals (Sweden)

    Hanguen Kim

    2015-05-01

    Full Text Available In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit, so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  9. Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

    Science.gov (United States)

    Kim, Hanguen; Lee, Sangwon; Lee, Dongsung; Choi, Soonmin; Ju, Jinsun; Myung, Hyun

    2015-05-26

    In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  10. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    OpenAIRE

    C. V. Subbulakshmi; Deepa, S. N.

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learni...

  11. Implementation of a classifier didactical machine for learning mechatronic processes

    Directory of Open Access Journals (Sweden)

    Alex De La Cruz

    2017-06-01

    Full Text Available The present article shows the design and construction of a classifier didactical machine through artificial vision. The implementation of the machine is to be used as a learning module of mechatronic processes. In the project, it is described the theoretical aspects that relate concepts of mechanical design, electronic design and software management which constitute popular field in science and technology, which is mechatronics. The design of the machine was developed based on the requirements of the user, through the concurrent design methodology to define and materialize the appropriate hardware and software solutions. LabVIEW 2015 was implemented for high-speed image acquisition and analysis, as well as for the establishment of data communication with a programmable logic controller (PLC via Ethernet and an open communications platform known as Open Platform Communications - OPC. In addition, the Arduino MEGA 2560 platform was used to control the movement of the step motor and the servo motors of the module. Also, is used the Arduino MEGA 2560 to control the movement of the stepper motor and servo motors in the module. Finally, we assessed whether the equipment meets the technical specifications raised by running specific test protocols.

  12. Improving the discriminatory power of a near-infrared microscopy spectral library with a support vector machine classifier.

    Science.gov (United States)

    Fernández-Ibáñez, V; Fearn, T; Montañés, E; Quevedo, J R; Soldado, A; de la Roza-Delgado, B

    2010-01-01

    A multi-group classifier based on the support vector machine (SVM) has been developed for use with a library of 48,456 spectra measured by near-infrared reflection microscopy (NIRM) on 227 samples representing 26 animal feed ingredients and 4 possible contaminants of animal origin. The performance of the classifier was assessed by a five-fold cross-validation, dividing at the sample level. Although the overall proportion of misclassifications was 27%, almost all of these involved the confusion of pairs of similar ingredients of vegetable origin. Such confusions are unimportant in the context of the intended use of the library, which is the detection of banned ingredients in animal feed. The error rate in discrimination between permitted and banned ingredients was just 0.17%. The performance of the SVM classifier was substantially better than that of the K-nearest-neighbors method employed in previous work with the same library, for which the comparable error rates are 36% overall and 0.39% for permitted versus banned ingredients.

  13. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  14. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

    Science.gov (United States)

    Subbulakshmi, C V; Deepa, S N

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  15. A Hybrid Prediction Method of Thermal Extension Error for Boring Machine Based on PCA and LS-SVM

    Directory of Open Access Journals (Sweden)

    Cheng Qiang

    2017-01-01

    Full Text Available Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA and least squares support vector machine (LS-SVM model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.

  16. Support Vector Machine for Discrimination Between Fault and Magnetizing Inrush Current in Power Transformer

    Directory of Open Access Journals (Sweden)

    V. Malathi

    2007-01-01

    Full Text Available This study presents a novel technique based on Support Vector Machine (SVM for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena in power transformer.

  17. Generalized SMO algorithm for SVM-based multitask learning.

    Science.gov (United States)

    Cai, Feng; Cherkassky, Vladimir

    2012-06-01

    Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

  18. Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

    Science.gov (United States)

    Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

    2017-01-01

    Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NBSW=NBBI-GRAM=SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we

  19. Semisupervised learning using Bayesian interpretation: application to LS-SVM.

    Science.gov (United States)

    Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

    2011-04-01

    Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

  20. Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

    Directory of Open Access Journals (Sweden)

    Gang Li

    2013-12-01

    Full Text Available Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC analysis and a support vector machine (SVM classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.

  1. Diagnostic accuracy of Parkinson disease by support vector machine (SVM) analysis of 123I-FP-CIT brain SPECT data: implications of putaminal findings and age.

    Science.gov (United States)

    Palumbo, Barbara; Fravolini, Mario Luca; Buresta, Tommaso; Pompili, Filippo; Forini, Nevio; Nigro, Pasquale; Calabresi, Paolo; Tambasco, Nicola

    2014-12-01

    Brain single-photon-emission-computerized tomography (SPECT) with I-ioflupane (I-FP-CIT) is useful to diagnose Parkinson disease (PD). To investigate the diagnostic performance of I-FP-CIT brain SPECT with semiquantitative analysis by Basal Ganglia V2 software (BasGan), we evaluated semiquantitative data of patients with suspect of PD by a support vector machine classifier (SVM), a powerful supervised classification algorithm.I-FP-CIT SPECT with BasGan analysis was performed in 90 patients with suspect of PD showing mild symptoms (bradykinesia-rigidity and mild tremor). PD was confirmed in 56 patients, 34 resulted non-PD (essential tremor and drug-induced Parkinsonism). A clinical follow-up of at least 6 months confirmed diagnosis. To investigate BasGan diagnostic performance we trained SVM classification models featuring different descriptors using both a "leave-one-out" and a "five-fold" method. In the first study we used as class descriptors the semiquantitative radiopharmaceutical uptake values in the left (L) and right (R) putamen (P) and in the L and R caudate nucleus (C) for a total of 4 descriptors (CL, CR, PL, PR). In the second study each patient was described only by CL and CR, while in the third by PL and PR descriptors. Age was added as a further descriptor to evaluate its influence in the classification performance.I-FP-CIT SPECT with BasGan analysis reached a classification performance higher than 73.9% in all the models. Considering the "Leave-one-out" method, PL and PR were better predictors (accuracy of 91% for all patients) than CL and CR descriptors; using PL, PR, CL, and CR diagnostic accuracy was similar to that of PL and PR descriptors in the different groups. Adding age as a further descriptor accuracy improved in all the models. The best results were obtained by using all the 5 descriptors both in PD and non-PD subjects (CR and CL + PR and PL + age = 96.4% and 94.1%, respectively). Similar results were observed for the "five

  2. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

    Science.gov (United States)

    Subasi, Abdulhamit

    2013-06-01

    Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.

  3. Applying Support Vector Machine in classifying satellite images for the assessment of urban sprawl

    Science.gov (United States)

    murgante, Beniamino; Nolè, Gabriele; Lasaponara, Rosa; Lanorte, Antonio; Calamita, Giuseppe

    2013-04-01

    In last decades the spreading of new buildings, road infrastructures and a scattered proliferation of houses in zones outside urban areas, produced a countryside urbanization with no rules, consuming soils and impoverishing the landscape. Such a phenomenon generated a huge environmental impact, diseconomies and a decrease in life quality. This study analyzes processes concerning land use change, paying particular attention to urban sprawl phenomenon. The application is based on the integration of Geographic Information Systems and Remote Sensing adopting open source technologies. The objective is to understand size distribution and dynamic expansion of urban areas in order to define a methodology useful to both identify and monitor the phenomenon. In order to classify "urban" pixels, over time monitoring of settlements spread, understanding trends of artificial territories, classifications of satellite images at different dates have been realized. In order to obtain these classifications, supervised classification algorithms have been adopted. More particularly, Support Vector Machine (SVM) learning algorithm has been applied to multispectral remote data. One of the more interesting features in SVM is the possibility to obtain good results also adopting few classification pixels of training areas. SVM has several interesting features, such as the capacity to obtain good results also adopting few classification pixels of training areas, a high possibility of configuration parameters and the ability to discriminate pixels with similar spectral responses. Multi-temporal ASTER satellite data at medium resolution have been adopted because are very suitable in evaluating such phenomena. The application is based on the integration of Geographic Information Systems and Remote Sensing technologies by means of open source software. Tools adopted in managing and processing data are GRASS GIS, Quantum GIS and R statistical project. The area of interest is located south of Bari

  4. 一种改进的多项式核支持向量机文本分类器%Improved polynomial SVM classifier for text categorization

    Institute of Scientific and Technical Information of China (English)

    熊忠阳; 江帆; 张玉芳; 李智星

    2009-01-01

    多项式核函数由于具有良好的泛化性能而受到重视,并被研究用于文本分类问题.针对多项式核学习能力较差的缺点,将学习能力较强的条件正定核与多项式核构成一个混合核函数作为改进的多项式核.实验表明,改进的多项式核SVM文本分类器的分类效果要好于多项式核SVM文本分类器.%Polynomial kernel is investigated and widely used for text categorization because of it' s high generalization performance. For the polynomial kernel low study performance' s fault, this paper combined conditionally positive definite kernel which had high study performance with polynomial kernel as an improved polynomial kernel. The experiment results show that the improved polynomial kernel SVM classifier for text categorization is superior to polynomial kernel SVM classifier for text categorization.

  5. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

    2017-01-01

    The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

  6. Predicting and Classifying User Identification Code System Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification (U ID) code that is the fingerprint for tracking the authorized user. The proposed hiding scheme makes use of a random number generator to scramble two copies of a UID,which will then be hidden in the randomly selected medium frequency coefficients of the host image. The linear support vector machine (SVM) will be used to train classifications by calculating the normalized correlation (NC) for the 2-class UID codes. The trained classifications will be the models used for identifying unreadable UID codes.Experimental results showed that the success of predicting the unreadable UID codes can be increased by applying SVM. The proposed scheme can be used to provide protections to intellectual property rights of digital images and to keep track of users to prevent collaborative piracies.

  7. Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

    Science.gov (United States)

    Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

    2011-01-01

    Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

  8. An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets.

    Science.gov (United States)

    Shi, Yingzhong; Chung, Fu-Lai; Wang, Shitong

    2015-09-01

    Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.

  9. A Study of BCI Signal Pattern Recognition by Using Quasi-Newton-SVM Method

    Institute of Scientific and Technical Information of China (English)

    YANG Chang-chun; MA Zheng-hua; SUN Yu-qiang; ZOU Ling

    2006-01-01

    The recognition of electroencephalogram (EEG) signals is the key of brain computer interface (BCI).Aimed at the problem that the recognition rate of EEG by using support vector machine (SVM) is low in BCI,based on the assumption that a well-defined physiological signal which also has a smooth form"hides" inside the noisy EEG signal,a Quasi-Newton-SVM recognition method based on Quasi-Newton method and SVM algorithm was presented.Firstly,the EEG signals were preprocessed by Quasi-Newton method and got the signals which were fit for SVM.Secondly,the preprocessed signals were classified by SVM method.The present simulation results indicated the Quasi-Newton-SVM approach improved the recognition rate compared with using SVM method; we also discussed the relationship between the artificial smooth signals and the classification errors.

  10. Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

    Directory of Open Access Journals (Sweden)

    Adrian Ion-Mărgineanu

    2017-07-01

    Full Text Available Purpose: The purpose of this study is classifying multiple sclerosis (MS patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS, 30 Relapse Remitting (RR, 17 Primary Progressive (PP, and 28 Secondary Progressive (SP] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale, conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA, Choline (Cho, and Creatine (Cre concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA, Support Vector Machines with gaussian kernel (SVM-rbf, and Random Forests.Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85% after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87% after training LDA and SVM

  11. A review and experimental study on application of classifiers and evolutionary algorithms in EEG based brain-machine interface systems.

    Science.gov (United States)

    Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

    2017-07-18

    Considering the importance and the near future development of noninvasive Brain-Machine Interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. The paper is divided into two main parts. In the first part a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and also evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, that is, Sensory Motor Rhythm-BMI (SMR-BMI) and Event Related Potentials-BMI (ERPs-BMI). Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, LDA (Linear Discriminant Analysis) and SVM (Support Vector Machines) with respect to CVA evaluation metric, and NB (Naive Bayes) with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers Bagg-DT (Bagging Decision Tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (Bagging Logistic Regression) and AdaBoost (Adaptive Boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective IWO (Invasive Weed Optimization) and bi-objective NSIWO (Nondominated Sorting IWO) algorithms demonstrated the best performances. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event related potentials) as well as their optimization

  12. Using machine learning to classify the diffuse interstellar bands

    CERN Document Server

    Baron, Dalya; Watson, Darach; Yao, Yushu; Cox, Nick L J; Prochaska, J Xavier

    2015-01-01

    Using over a million and a half extragalactic spectra we study the correlations of the Diffuse Interstellar Bands (DIBs) in the Milky Way. We measure the correlation between DIB strength and dust extinction for 142 DIBs using 24 stacked spectra in the reddening range E(B-V) < 0.2, many more lines than ever studied before. Most of the DIBs do not correlate with dust extinction. However, we find 10 weak and barely studied DIBs with correlations that are higher than 0.7 with dust extinction and confirm the high correlation of additional 5 strong DIBs. Furthermore, we find a pair of DIBs, 5925.9A and 5927.5A which exhibits significant negative correlation with dust extinction, indicating that their carrier may be depleted on dust. We use Machine Learning algorithms to divide the DIBs to spectroscopic families based on 250 stacked spectra. By removing the dust dependency we study how DIBs follow their local environment. We thus obtain 6 groups of weak DIBs, 4 of which are tightly associated with C2 or CN absorp...

  13. SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

    Science.gov (United States)

    Li, Ying Hong; Xu, Jing Yu; Tao, Lin; Li, Xiao Feng; Li, Shuang; Zeng, Xian; Chen, Shang Ying; Zhang, Peng; Qin, Chu; Zhang, Cheng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

    2016-01-01

    Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

  14. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  15. Seizure prediction using polynomial SVM classification.

    Science.gov (United States)

    Zisheng Zhang; Parhi, Keshab K

    2015-08-01

    This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients with low hardware complexity and low power consumption. In the proposed approach, we first compute the spectrogram of the input fragmented EEG signals from a few electrodes. Each fragmented data clip is ten minutes in duration. Band powers, relative spectral powers and ratios of spectral powers are extracted as features. The features are then subjected to electrode selection and feature selection using classification and regression tree. The baseline experiment uses all features from selected electrodes and these features are then subjected to a radial basis function kernel support vector machine (RBF-SVM) classifier. The proposed method further selects a small number features from the selected electrodes and train a polynomial support vector machine (SVM) classifier with degree of 2 on these features. Prediction performances are compared between the baseline experiment and the proposed method. The algorithm is tested using intra-cranial EEG (iEEG) from the American Epilepsy Society Seizure Prediction Challenge database. The baseline experiment using a large number of features and RBF-SVM achieves a 100% sensitivity and an average AUC of 0.9985, while the proposed algorithm using only a small number of features and polynomial SVM with degree of 2 can achieve a sensitivity of 100.0%, an average area under curve (AUC) of 0.9795. For both experiments, only 10% of the available training data are used for training.

  16. On the Use of Time–Frequency Reassignment and SVM-Based Classifier for Audio Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Souli S. Sameh

    2014-11-01

    Full Text Available In this paper, we propose a robust environmental sound spectrogram classification approach. Its purpose is surveillance and security applications based on the reassignment method and log-Gabor filters. Besides, the reassignment method is applied to the spectrogram to improve the readability of the time-frequency representation, and to assure a better localization of the signal components. Our approach includes three methods. In the first two methods, the reassigned spectrograms are passed through appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criterion. The third method uses the same steps but applied only to three patches extracted from each reassigned spectrogram. The proposed approach is tested on a large database consists of 1000 sounds belonging to ten classes. The recognition is based on Multiclass Support Vector Machines.

  17. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  18. Statistical and Machine-Learning Classifier Framework to Improve Pulse Shape Discrimination System Design

    Energy Technology Data Exchange (ETDEWEB)

    Wurtz, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kaplan, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-28

    Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-­realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-­building elements and their functions in a fully-­designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.

  19. Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

    Directory of Open Access Journals (Sweden)

    Sara Attarchi

    2014-04-01

    Full Text Available Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM, neural networks (NN, random forest (RF and one traditional classifier (i.e., maximum likelihood classification (MLC as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA = 65%. This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%. However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%. The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%. However, this shows a slight improvement compared to the ETM+ classification (OA = 84%. The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data.

  20. Win percentage: a novel measure for assessing the suitability of machine classifiers for biological problems

    Science.gov (United States)

    2012-01-01

    Background Selecting an appropriate classifier for a particular biological application poses a difficult problem for researchers and practitioners alike. In particular, choosing a classifier depends heavily on the features selected. For high-throughput biomedical datasets, feature selection is often a preprocessing step that gives an unfair advantage to the classifiers built with the same modeling assumptions. In this paper, we seek classifiers that are suitable to a particular problem independent of feature selection. We propose a novel measure, called "win percentage", for assessing the suitability of machine classifiers to a particular problem. We define win percentage as the probability a classifier will perform better than its peers on a finite random sample of feature sets, giving each classifier equal opportunity to find suitable features. Results First, we illustrate the difficulty in evaluating classifiers after feature selection. We show that several classifiers can each perform statistically significantly better than their peers given the right feature set among the top 0.001% of all feature sets. We illustrate the utility of win percentage using synthetic data, and evaluate six classifiers in analyzing eight microarray datasets representing three diseases: breast cancer, multiple myeloma, and neuroblastoma. After initially using all Gaussian gene-pairs, we show that precise estimates of win percentage (within 1%) can be achieved using a smaller random sample of all feature pairs. We show that for these data no single classifier can be considered the best without knowing the feature set. Instead, win percentage captures the non-zero probability that each classifier will outperform its peers based on an empirical estimate of performance. Conclusions Fundamentally, we illustrate that the selection of the most suitable classifier (i.e., one that is more likely to perform better than its peers) not only depends on the dataset and application but also on the

  1. 基于混沌遗传算法的模糊LS-SVM分类器及其应用%Fuzzy LS-SVM Classifier Based on Chaos Genetic Algorithm and Its Application

    Institute of Scientific and Technical Information of China (English)

    王禾军; 邓飞其; 陈治明

    2011-01-01

    为克服支持向量机算法对噪声点和异常点的敏感性,采用清晰集合构造模糊集合法确定隶属度,采用混沌遗传算法优化参数的模糊最小二乘支持向量机分类器(FLS-SVMBCGA),并用著名的Ripley数据集、MONK数据集和PIMA数据集进行了数值实验,对油气输送管道的TPD检测信号进行了诊断.结果表明,FLS-SVMBCGA分类器能有效提高带噪声点和异常点数据集分类的预测精度,对油气输送管道的TPD信号分类效果高于91.67%,可实现对油气输送管道TPD信号的准确诊断.%In order to reduce the sensitivity of the support vector machines (SVM) to noise and outliers, a new fuzzy least squares-support vector machines classifier based on chaos genetic algorithm is proposed and is abbreviated to FLS-SVMBCGA, in which the clear sets are used to construct a fuzzy membership set and the chaos genetic algorithm is adopted to optimize the parameters. Then, some experiments are carried ont on three benchmarking datasets such as the Ripley dataset, the MONK dataset and the PIMA dataset. Finally, the TPD signals from oil and gas transmission pipeline are diagnosed using the proposed classifier. The results show that FLS-SVMBCGA is effective in improving the prediction accuracy of the classification problems with noises or outliers, with a classifying effect for TPD signals being higher than 91.67%, which means that the proposed algorithm can accurately diagnose the TPD signals from oil and gas transmission pipeline.

  2. Data characteristics that determine classifier performance

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2006-11-01

    Full Text Available classifiers. 10-fold cross-validation is used to evaluate and compare the performance of the classifiers on the different data sets. 3.1. Artificial data generation Multivariate Gaussian distributions are used to generate artificial data sets. We use d...NN) classifier [8], the multi- layer perceptron (MLP) and support vector machines (SVMs) [9]. The NB, DT, kNN, MLP and SVM classifiers are all implementations of the machine learning package Weka [10]. The Gaussian classifier is a Matlab implementation...

  3. Classifying Uncertain and Evolving Data Streams with Distributed Extreme Learning Machine

    Institute of Scientific and Technical Information of China (English)

    韩东红; 张昕; 王国仁

    2015-01-01

    Conventional classification algorithms are not well suited for the inherent uncertainty, potential concept drift, volume, and velocity of streaming data. Specialized algorithms are needed to obtain efficient and accurate classifiers for uncertain data streams. In this paper, we first introduce Distributed Extreme Learning Machine (DELM), an optimization of ELM for large matrix operations over large datasets. We then present Weighted Ensemble Classifier Based on Distributed ELM (WE-DELM), an online and one-pass algorithm for efficiently classifying uncertain streaming data with concept drift. A probability world model is built to transform uncertain streaming data into certain streaming data. Base classifiers are learned using DELM. The weights of the base classifiers are updated dynamically according to classification results. WE-DELM improves both the efficiency in learning the model and the accuracy in performing classification. Experimental results show that WE-DELM achieves better performance on different evaluation criteria, including efficiency, accuracy, and speedup.

  4. AN ADAPTIVE REGION GROWING ALGORITHM WITH SUPPORT VECTOR MACHINE CLASSIFIER FOR TUBERCULOSIS CAVITY IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    K. Meenakshi Sundaram

    2013-01-01

    Full Text Available The major world health menace is Tuberculosis (TB that has the effect on one-third of the global population and millions of new infections are occurring every year. The cavities in the upper lung zone are a strong indicator that the disease has developed into a highly contagious state. The study includes 52 Chest X-ray images with Tuberculosis and 43 Chest X-ray images without Tuberculosis. The identification of the TB cavities is mostly conducted by the clinicians by observing the chest radiographs. But the automatic screening has lot of advantages such as substantial reduction in the labor workload of clinicians, enhancing the sensitivity of the test and better precision in diagnosis by increasing the number of images that can be analyzed by the computer. Many researchers have proposed different techniques to improve the performance of automatic screening process. This paper improves the accuracy over the existing technique using the adaptive region growing property and SVM classifier. Initially, pre-processing is carried out for the input image using Gaussian filtering technique to make the image suitable for further processing. The contours of the image will be obtained using region growing technique. The SVM classifier is then used to confirm the suspected TB cavities. The classification will be carried out by the features which the study has taken from the segmented image. The proposed technique is implemented in MATLAB and the performance is compared with the existing technique. From the result, the study has achieved eighty-five percentage accuracy over the existing technique’s seventy eight-percentage accuracy.

  5. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-06-28

    A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is

  6. On the generalizability of resting-state fMRI machine learning classifiers.

    Science.gov (United States)

    Huf, Wolfgang; Kalcher, Klaudius; Boubela, Roland N; Rath, Georg; Vecsei, Andreas; Filzmoser, Peter; Moser, Ewald

    2014-01-01

    Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.

  7. Forecasting Financial Distress of Chinese High-tech Manufacturing Companies Based on a Hybrid Model of GA-SVM

    Institute of Scientific and Technical Information of China (English)

    SONG Xin-ping; DING Yong-sheng; GE Yan; LONG Quan

    2008-01-01

    Owing to the radical changing of Chinese economy, it is essential to build an effective financial distress prediction model. In this paper, we present a genetic algorithm (GA) approach for optimizing parameters of support vector machine (SVM). We validate the proposed model on datasets of Chinese high-tech manufacturing industry. Experimental results reveal that the proposed GA-SVM model can compare to and even outperform other exiting classifiers. Compared to grid-search algorithm, the proposed GA-based takes less time to optimize SVM parameter without degrading the prediction accuracy of SVM.

  8. Web Page Classification using an ensemble of support vector machine classifiers

    Directory of Open Access Journals (Sweden)

    Shaobo Zhong

    2011-11-01

    Full Text Available Web Page Classification (WPC is both an important and challenging topic in data mining. The knowledge of WPC can help users to obtain useable information from the huge internet dataset automatically and efficiently. Many efforts have been made to WPC. However, there is still room for improvement of current approaches. One particular challenge in training classifiers comes from the fact that the available dataset is usually unbalanced. Standard machine learning algorithms tend to be overwhelmed by the major class and ignore the minor one and thus lead to high false negative rate. In this paper, a novel approach for Web page classification was proposed to address this problem by using an ensemble of support vector machine classifiers to perform this work. Principal Component Analysis (PCA is used for feature reduction and Independent Component Analysis (ICA for feature selection. The experimental results indicate that the proposed approach outperforms other existing classifiers widely used in WPC.

  9. A NOVEL MULTICLASS SUPPORT VECTOR MACHINE ALGORITHM USING MEAN REVERSION AND COEFFICIENT OF VARIANCE

    Directory of Open Access Journals (Sweden)

    Bhusana Premanode

    2013-01-01

    Full Text Available Inaccuracy of a kernel function used in Support Vector Machine (SVM can be found when simulated with nonlinear and stationary datasets. To minimise the error, we propose a new multiclass SVM model using mean reversion and coefficient of variance algorithm to partition and classify imbalance in datasets. By introducing a series of test statistic, simulations of the proposed algorithm outperformed the performance of the SVM model without using multiclass SVM model.

  10. The application of discriminant analysis and Machine Learning methods as tools to identify and classify compounds with potential as transdermal enhancers.

    Science.gov (United States)

    Moss, G P; Shah, A J; Adams, R G; Davey, N; Wilkinson, S C; Pugh, W J; Sun, Y

    2012-01-23

    Discriminant analysis (DA) has previously been shown to allow the proposal of simple guidelines for the classification of 73 chemical enhancers of percutaneous absorption. Pugh et al. employed DA to classify such enhancers into simple categories, based on the physicochemical properties of the enhancer molecules (Pugh et al., 2005). While this approach provided a reasonable accuracy of classification it was unable to provide a consistently reliable estimate of enhancement ratio (ER, defined as the amount of hydrocortisone transferred after 24h, relative to control). Machine Learning methods, including Gaussian process (GP) regression, have recently been employed in the prediction of percutaneous absorption of exogenous chemicals (Moss et al., 2009; Lam et al., 2010; Sun et al., 2011). They have shown that they provide more accurate predictions of these phenomena. In this study several Machine Learning methods, including the K-nearest-neighbour (KNN) regression, single layer networks, radial basis function networks and the SVM classifier were applied to an enhancer dataset reported previously. The SMOTE sampling method was used to oversample chemical compounds with ER>10 in each training set in order to improve estimation of GP and KNN. Results show that models using five physicochemical descriptors exhibit better performance than those with three features. The best classification result was obtained by using the SVM method without dealing with imbalanced data. Following over-sampling, GP gives the best result. It correctly assigned 8 of the 12 "good" (ER>10) enhancers and 56 of the 59 "poor" enhancers (ERMachine Learning methods are that they can provide more accurate classification of enhancer type with fewer false-positive results and that, unlike discriminant analysis, they are able to make predictions of enhancer ability.

  11. An Optimal SVM with Feature Selection Using Multiobjective PSO

    Directory of Open Access Journals (Sweden)

    Iman Behravan

    2016-01-01

    Full Text Available Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, C, and the kernel factor, σ. Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM. The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks.

  12. Classifying cognitive profiles using machine learning with privileged information in Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Hanin Hamdan Alahmadi

    2016-11-01

    Full Text Available Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ classifiers to discriminate patients with Mild Cognitive Impairment (MCI from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1 when overall fMRI signal for structured stimuli is

  13. Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

    Science.gov (United States)

    Alahmadi, Hanin H.; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B.; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

    2016-01-01

    Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a “Learning with privileged information” approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post

  14. Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

    Science.gov (United States)

    Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

    2016-01-01

    Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post

  15. An Empirical Study of Machine Learning Techniques for Classifying Emotional States from EEG Data

    OpenAIRE

    2012-01-01

    With the great advancement in robot technology, smart human-robot interaction is considered to be the most wanted success by the researchers these days. If a robot can identify emotions and intentions of a human interacting with it, that would make robots more useful. Electroencephalography (EEG) is considered one effective way of recording emotions and motivations of a human using brain. Various machine learning techniques are used successfully to classify EEG data accurately. K-Nearest Neig...

  16. Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform

    Directory of Open Access Journals (Sweden)

    Nikolas eKessler

    2015-03-01

    Full Text Available We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious on the background of nowadays increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to eleven wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout three years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA and supervised (RF, SVM methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show, that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms.

  17. Effects of brushing in a classifying machine on the cuticles of Fuji and Gala apples

    Directory of Open Access Journals (Sweden)

    Renar João Bender

    2009-06-01

    Full Text Available The cuticle, a layer that covers the fruit epidermis, has a protective function against environmental stresses such as wind, temperature, chemicals and drought, not only when the fruit is attached to the plant, but also after harvest. Some postharvest procedures may influence the external layers of the fruit, like the cuticle. The objective of this work was to evaluate the effects of brushing in a classifying machine on the cuticles of apples under scanning electron microscopy (SEM. Two experiments were conducted to test brushing on the cultivars Fuji and Gala using heavy and smooth brushes. The experiments consisted of three replicates of three apples each, with three samples taken from the equatorial area of the fruit to be analyzed under SEM. The brushes of the classifying machine altered the cuticular layer, dragging it, modifying the structure and removing crystalloids of the cuticular wax layer, and forming cracks. There were no differences between the effects of the two types of brushes tested on the cuticles of the apples. The classifying machine used commercially is capable of producing similar effects to those encountered in the brushing experiments conducted on the prototype in the laboratory, removing partially the protective wax content of the apple’s cuticle.

  18. Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

    Science.gov (United States)

    Wang, Xianfang; Du, Haoze; Tan, Jinglu

    2016-12-01

    Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

  19. Linear SVM-Based Android Malware Detection for Reliable IoT Services

    Directory of Open Access Journals (Sweden)

    Hyo-Sik Ham

    2014-01-01

    Full Text Available Current many Internet of Things (IoT services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

  20. A machine learned classifier for RR Lyrae in the VVV survey

    Science.gov (United States)

    Elorrieta, Felipe; Eyheramendy, Susana; Jordán, Andrés; Dékány, István; Catelan, Márcio; Angeloni, Rodolfo; Alonso-García, Javier; Contreras-Ramos, Rodrigo; Gran, Felipe; Hajdu, Gergely; Espinoza, Néstor; Saito, Roberto K.; Minniti, Dante

    2016-11-01

    Variable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and particularly those of the more easily recognized type ab (i.e., fundamental-mode pulsators), from the 106-107 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.

  1. A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA

    CERN Document Server

    Wagstaff, Kiri L; Thompson, David R; Khudikyan, Shakeh; Wyngaard, Jane; Deller, Adam T; Palaniswamy, Divya; Tingay, Steven J; Wayth, Randall B

    2016-01-01

    Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts (FRBs) within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now c...

  2. Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

    Science.gov (United States)

    Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

    2015-01-01

    Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

  3. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-01-01

    Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

  4. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

    Science.gov (United States)

    Cho, Ming-Yuan; Hoang, Thi Thom

    2017-01-01

    Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

  5. A Hybrid RBF-SVM Ensemble Approach for Data Mining Applications

    Directory of Open Access Journals (Sweden)

    M.Govindarajan

    2014-02-01

    Full Text Available One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for data mining applications like intrusion detection, direct marketing, and signature verification. In this research work, new hybrid classification method is proposed for heterogeneous ensemble classifiers using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using a Radial Basis Function (RBF and Support Vector Machine (SVM as base classifiers. Here, modified training sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. The proposed RBF-SVM hybrid system is superior to individual approach for intrusion detection, direct marketing, and signature verification in terms of classification accuracy.

  6. A machine learned classifier for RR Lyrae in the VVV survey

    CERN Document Server

    Elorrieta, Felipe; Jordán, Andrés; Dékány, István; Catelan, Márcio; Angeloni, Rodolfo; Alonso-García, Javier; Contreras-Ramos, Rodrigo; Gran, Felipe; Hajdu, Gergely; Espinoza, Néstor; Saito, Roberto K; Minniti, Dante

    2016-01-01

    Variable stars of RR Lyrae type are a prime tool to obtain distances to old stellar populations in the Milky Way, and one of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Due to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae,and particularly those of the more easily recognized type ab (i.e., fundamental-mode pulsators), from the 10^6-10^7 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a K_s-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaB...

  7. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Science.gov (United States)

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

  8. [Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

    Science.gov (United States)

    Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

    2013-03-01

    Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

  9. An iris recognition method based on multi-orientation features and Non-symmetrical SVM

    Institute of Scientific and Technical Information of China (English)

    GU Hong-ying; ZHUANG Yue-ting; PAN Yun-he

    2005-01-01

    A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each sub-image and used to train Support Vector Machine (SVM) as iris classifiers. SVM has drawn great interest recently as one of the best classifiers in machine learning, although there is a problem in the use of traditional SVM for iris recognition. It cannot treat False Accept and False Reject differently with different security requirements. Therefore, a new kind of SVM called Non-symmetrical SVM is presented to classify the iris features. Experimental data shows that Non-symmetrical SVM can satisfy various security requirements in iris recognition applications. Feature sequence combined with spatial and frequency domain represents the variation details of the iris patterns properly. The results in this study demonstrate the potential of our new approach, and show that it performs more satisfactorily when compared to former algorithms.

  10. [Classification technique for hyperspectral image based on subspace of bands feature extraction and LS-SVM].

    Science.gov (United States)

    Gao, Heng-zhen; Wan, Jian-wei; Zhu, Zhen-zhen; Wang, Li-bao; Nian, Yong-jian

    2011-05-01

    The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM (least squares support vector machine). The LS-SVM uses the features extracted from subspace of bands (SOB). The maximum noise fraction (MNF) method is adopted as the feature extraction method. The spectral correlations of the hyperspectral image are used in order to divide the feature space into several SOBs. Then the MNF is used to extract characteristic features of the SOBs. The extracted features are combined into the feature vector for classification. So the strong bands correlation is avoided and the spectral redundancies are reduced. The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved. The proposed method optimizes spectral information by feature extraction and reduces the spectral noise. The classifier performance is improved. Experimental results show the superiorities of the proposed algorithm.

  11. Hyperspectral remote sensing image classification based on combined SVM and LDA

    Science.gov (United States)

    Zhang, Chunsen; Zheng, Yiwei

    2014-11-01

    This paper presents a novel method for hyperspectral image classification based on the minimum noise fraction (MNF) and an approach combining support vector machine (SVM) and linear discriminant analysis (LDA). A new SVM/LDA algorithm is used for the classification. First, we use MNF method to reduce the dimension and extract features of the image, and then use the SVM/LDA algorithm to transform the extracted features. Next, we train the result of transformation, optimize the parameters through cross-validation and grid search method, then get a optimal hyperspectral image classifier. Finally, we use this classifier to complete classification. In order to verify the proposed method, the AVIRIS Indian Pines image was used. The experimental results show that the proposed method can solve the contradiction between the small amount of samples and high dimension, improve classification accuracy compared to the classical SVM method.

  12. 基于支持向量机与无监督聚类相结合的中文网页分类器%A Chinese Web Page Classifier Based on Support Vector Machine and Unsupervised Clustering

    Institute of Scientific and Technical Information of China (English)

    李晓黎; 刘继敏; 史忠植

    2001-01-01

    This paper presents a new algorithm that combines Support VectorMachine (SVM) and unsupervised clustering. After analyzing the characteristics of web pages, it proposes a new vector representation of web pages and applies it to web page classification. Given a training set, the algorithm clusters positive and negative examples respectively by the unsupervised clustering algorithm (UC), which will produce a number of positive and negative centers. Then, it selects only some of the examples to input to SVM according to ISUC algorithm. At the end, it constructs a classifier through SVM learning. Any text can be classified by comparing the distance of clustering centers or by SVM. If the text nears one cluster center of a category and far away from all the cluster centers of other categories, UC can classify it rightly with high possibility, otherwise SVM is employed to decide the category it belongs. The algorithm utilizes the virtues of SVM and unsupervised clustering. The experiment shows that it not only improves training efficiency, but also has good precision.%提出了一种将支持向量机与无监督聚类相结合的新分类算法,给出了一种新的网页表示方法并应用于网页分类问题.该算法首先利用无监督聚类分别对训练集中正例和反例聚类,然后挑选一些例子训练SVM并获得SVM分类器.任何网页可以通过比较其与聚类中心的距离决定采用无监督聚类方法或SVM分类器进行分类.该算法充分利用了SVM准确率高与无监督聚类速度快的优点.实验表明它不仅具有较高的训练效率,而且有很高的精确度.

  13. Efficient Discriminate Component Analysis using Support Vector Machine Classifier on Invariant Pose and Illumination Face Images

    Directory of Open Access Journals (Sweden)

    R. Rajalakshmi

    2015-03-01

    Full Text Available Face recognition is the process of categorizing a person in an image by evaluating with a known face image library. The pose and illumination variations are two main practical confronts for an automatic face recognition system. This study proposes a novel face recognition algorithm known as Efficient Discriminant Component Analysis (EDCA for face recognition under varying poses and illumination conditions. This EDCA algorithm overcomes the high dimensionality problem in the feature space by extracting features from the low dimensional frequency band of the image. It combines the features of both LDA and PCA algorithms and these features are used in the training set and is classified using Support Vector Machine classifier. The experiments were performed on the CMU-PIE datasets. The experimental results show that the proposed algorithm produces a higher recognition rate than the existing LDA and PCA based face recognition techniques.

  14. A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA

    Science.gov (United States)

    Wagstaff, Kiri L.; Tang, Benyang; Thompson, David R.; Khudikyan, Shakeh; Wyngaard, Jane; Deller, Adam T.; Palaniswamy, Divya; Tingay, Steven J.; Wayth, Randall B.

    2016-08-01

    Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%-90% of the candidates, with an accuracy greater than 98%, leaving only the 10%-20% most promising candidates to be reviewed by humans.

  15. SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

    Science.gov (United States)

    Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

  16. Osteoporosis Recognition Based on Similarity Metric with SVM

    Directory of Open Access Journals (Sweden)

    Ke Zhou

    2016-06-01

    Full Text Available The purpose: Applying different techniques of classification to osteoporotic bone tissue texture analysis, exploring the recognition rate of the different classification methods. Methods: Using gray-level co-occurrence matrix (GLCM and running a length matrix texture analysis to extract bone tissue slice image characteristic parameters, and to classify respectively 4x and 10x microscope images of the two groups: the sham (SHAM and the ovariectomized (OVX group image. Results: The metric support vector machine (SVM classification algorithm, based on SVM learning or recognition rate, was higher than the stand-alone measure, and the classification results were stable. Conclusion: Measurement of the SVM classification algorithm for osteoporotic bone slices texture analysis revealed a high recognition rate.

  17. 3D optical coherence tomography super pixel with machine classifier analysis for glaucoma detection.

    Science.gov (United States)

    Xu, Juan; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel S

    2011-01-01

    Current standard quantitative 3D spectral-domain optical coherence tomography (SD-OCT) analyses of various ocular diseases is limited in detecting structural damage at early pathologic stages. This is mostly because only a small fraction of the 3D data is used in the current method of quantifying the structure of interest. This paper presents a novel SD-OCT data analysis technique, taking full advantage of the 3D dataset. The proposed algorithm uses machine classifier to analyze SD-OCT images after grouping adjacent pixels into super pixel in order to detect glaucomatous damage. A 3D SD-OCT image is first converted into a 2D feature map and partitioned into over a hundred super pixels. Machine classifier analysis using boosting algorithm is performed on super pixel features. One hundred and ninety-two 3D OCT images of the optic nerve head region were tested. Area under the receiver operating characteristic (AUC) was computed to evaluate the glaucoma discrimination performance of the algorithm and compare it to the commercial software output. The AUC of normal vs glaucoma suspect eyes using the proposed method was statistically significantly higher than the current method (0.855 and 0.707, respectively, p=0.031). This new method has the potential to improve early detection of glaucomatous structural damages.

  18. Machine-learning approaches for classifying haplogroup from Y chromosome STR data.

    Directory of Open Access Journals (Sweden)

    Joseph Schlecht

    2008-06-01

    Full Text Available Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs. Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.

  19. Stacking machine learning classifiers to identify Higgs bosons at the LHC

    Science.gov (United States)

    Alves, A.

    2017-05-01

    Machine learning (ML) algorithms have been employed in the problem of classifying signal and background events with high accuracy in particle physics. In this paper, we compare the performance of a widespread ML technique, namely, stacked generalization, against the results of two state-of-art algorithms: (1) a deep neural network (DNN) in the task of discovering a new neutral Higgs boson and (2) a scalable machine learning system for tree boosting, in the Standard Model Higgs to tau leptons channel, both at the 8 TeV LHC. In a cut-and-count analysis, stacking three algorithms performed around 16% worse than DNN but demanding far less computation efforts, however, the same stacking outperforms boosted decision trees. Using the stacked classifiers in a multivariate statistical analysis (MVA), on the other hand, significantly enhances the statistical significance compared to cut-and-count in both Higgs processes, suggesting that combining an ensemble of simpler and faster ML algorithms with MVA tools is a better approach than building a complex state-of-art algorithm for cut-and-count.

  20. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

    Science.gov (United States)

    Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

    2012-12-05

    Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pnetwork topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

  1. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  2. Automotic Recognition of Sleep Spindles Based on Two-Stage Classifier with Artificial Neural Networks and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    MohammadHoseyn Khaksar

    2008-03-01

    Full Text Available Sleep spindles are one of the most important transient waveforms found in the sleep EEG signal. Here, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SS in a 19-channel electroencephalographic signal. In the first stage, a pre-processing perception is used for enhancing overall detection and also reducing computation time. In the second stage, the selected Sleep spindles (SS, classified with neural network post-classifier. Classifying tools in post-processing procedure were MLP and RBSVM that their operations are compared in the last section of the report. Visual inspection of 19-channel EEG from six subjects by one expert in this theme, showed that RBSVM operation is better than MLP with BP (Back propagation training, that SVM provided 91.4% average sensitivity and 3.85% average false detection rate.

  3. Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

    Directory of Open Access Journals (Sweden)

    Roland Zemp

    2016-01-01

    Full Text Available Occupational musculoskeletal disorders, particularly chronic low back pain (LBP, are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user’s sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest. Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples. Sixteen force sensor values and the backrest angle were used as the explanatory variables (features for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

  4. [LLE-SVM classification of apple mealiness based on hyperspectral scattering image].

    Science.gov (United States)

    Zhao, Gui-lin; Zhu, Qi-bing; Huang, Min

    2010-10-01

    Apple mealiness degree is an important factor for its internal quality. hyperspectral scattering, as a promising technique, was investigated for noninvasive measurement of apple mealiness. In the present paper, a locally linear embedding (LLE) coupled with support vector machine (SVM) was proposed to achieve classification because of large number of image data. LLE is a nonlinear lowering dimension method, which reveals the structure of the global nonlinearity by the local linear joint. This method can effectively calculate high-dimensional input data embedded in a low-dimensional space manifold. The dimension reduction of hyperspectral data was classified by SVM. Comparing the LLE-SVM classification method with the traditional SVM classification, the results indicated that the training accuracy obtained with the LLE-SVM was higher than that just with SVM; and the testing accuracy of the classifier changed a little before and after dimensionality reduction, and the range of fluctuation was less than 5%. It is expected that LLE-SVM method would provide an effective classification method for apple mealiness nondestructive detection using hyperspectral scattering image technique.

  5. 一种基于 QBC 的 SVM 主动学习算法%Active learning algorithm for SVM based on QBC

    Institute of Scientific and Technical Information of China (English)

    徐海龙; 别晓峰; 冯卉; 吴天爱

    2015-01-01

    To the problem that large-scale labeled samples is not easy to acquire and the class-unbalanced dataset in the course of souport vector machine (SVM)training,an active learning algorithm based on query by committee (QBC)for SVM(QBC-ASVM)is proposed,which efficiently combines the improved QBC active learning and the weighted SVM.In this method,QBC active learning is used to select the samples which are the most valuable to the current SVM classifier,and the weighted SVM is used to reduce the impact of the unba-lanced data set on SVMs active learning.The experimental results show that the proposed approach can consid-erably reduce the labeled samples and costs compared with the passive SVM,and at the same time,it can ensure that the accurate classification performance is kept as the passive SVM,and the proposed method improves gen-eralization performance and also expedites the SVM training.%针对支持向量机(souport vector machine,SVM)训练学习过程中样本分布不均衡、难以获得大量带有类标注样本的问题,提出一种基于委员会投票选择(query by committee,QBC)的 SVM 主动学习算法 QBC-AS-VM,将改进的 QBC 主动学习方法与加权 SVM 方法有机地结合应用于 SVM 训练学习中,通过改进的 QBC 主动学习,主动选择那些对当前 SVM 分类器最有价值的样本进行标注,在 SVM 主动学习中应用改进的加权 SVM,减少了样本分布不均衡对 SVM 主动学习性能的影响,实验结果表明在保证不影响分类精度的情况下,所提出的算法需要标记的样本数量大大少于随机采样法需要标记的样本数量,降低了学习的样本标记代价,提高了 SVM 泛化性能而且训练速度同样有所提高。

  6. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

    Directory of Open Access Journals (Sweden)

    Meysam Bastani

    Full Text Available BACKGROUND: Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. METHODS: To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. RESULTS: This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. CONCLUSIONS: Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.

  7. A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

    Science.gov (United States)

    Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

    2013-01-01

    Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

  8. Classification of underwater still objects based on multi-field features and SVM

    Institute of Scientific and Technical Information of China (English)

    TIAN Jie; XUE Shan-hua; HUANG Hai-ning; ZHANG Chun-hua

    2007-01-01

    A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.

  9. A new expert system for diagnosis of lung cancer: GDA-LS_SVM.

    Science.gov (United States)

    Avci, Engin

    2012-06-01

    In nowadays, there are many various diseases, whose diagnosis is very hardly. Lung cancer is one of this type diseases. It begins in the lungs and spreads to other organs of human body. In this paper, an expert diagnostic system based on General Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM) Classifier for diagnosis of lung cancer. This expert diagnosis system is called as GDA-LS-SVM in rest of this paper. The GDA-LS-SVM expert diagnosis system has two stages. These are 1. Feature extraction and feature reduction stage and 2. Classification stage. In feature extraction and feature reduction stage, lung cancer dataset is obtained and dimension of this lung cancer dataset, which has 57 features, is reduced to eight features using Generalized Discriminant Analysis (GDA) method. Then, in classification stage, these reduced features are given to Least Squares Support Vector Machine (LS-SVM) classifier. The lung cancer dataset used in this study was taken from the UCI machine learning database. The classification accuracy of this GDA-LS-SVM expert system was obtained about 96.875% from results of these experimental studies.

  10. Binary classification of ¹⁸F-flutemetamol PET using machine learning

    DEFF Research Database (Denmark)

    Vandenberghe, Rik; Nelissen, Natalie; Salmon, Eric

    2013-01-01

    (18)F-flutemetamol is a positron emission tomography (PET) tracer for in vivo amyloid imaging. The ability to classify amyloid scans in a binary manner as 'normal' versus 'Alzheimer-like', is of high clinical relevance. We evaluated whether a supervised machine learning technique, support vector...... machines (SVM), can replicate the assignments made by visual readers blind to the clinical diagnosis, which image components have highest diagnostic value according to SVM and how (18)F-flutemetamol-based classification using SVM relates to structural MRI-based classification using SVM within the same...

  11. Robust support vector machine-trained fuzzy system.

    Science.gov (United States)

    Forghani, Yahya; Yazdi, Hadi Sadoghi

    2014-02-01

    Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.

  12. Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

    Directory of Open Access Journals (Sweden)

    N. Pérez-Díaz

    2016-01-01

    Full Text Available Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin, which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

  13. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  14. A comparison of rule-based and machine learning approaches for classifying patient portal messages.

    Science.gov (United States)

    Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

    2017-09-01

    Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard

  15. The Application of Support Vector Machine (svm) Using Cielab Color Model, Color Intensity and Color Constancy as Features for Ortho Image Classification of Benthic Habitats in Hinatuan, Surigao del Sur, Philippines

    Science.gov (United States)

    Cubillas, J. E.; Japitana, M.

    2016-06-01

    This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  16. THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    J. E. Cubillas

    2016-06-01

    Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  17. Support vector machine ensemble using rough sets theory

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A support vector machine (SVM) ensemble classifier is proposed. Performance of SVM trained in an input space consisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decisionmaking of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.

  18. Construction of Classifier Based on MPCA and QSA and Its Application on Classification of Pancreatic Diseases

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2013-01-01

    Full Text Available A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA. In order to verify the effectiveness of the proposed algorithm, the normal SVM method has been chosen as comparing algorithm. The experimental results show that the proposed method can effectively extract the eigenfeatures and improve the classification accuracy of pancreatic images.

  19. Comparison of SVM RBF-NN and DT for crop and weed identification based on spectral measurement over corn fields

    Science.gov (United States)

    It is important to find an appropriate pattern-recognition method for in-field plant identification based on spectral measurement in order to classify the crop and weeds accurately. In this study, the method of Support Vector Machine (SVM) was evaluated and compared with two other methods, Decision ...

  20. Texture discrimination of green tea categories based on least squares support vector machine (LSSVM) classifier

    Science.gov (United States)

    Li, Xiaoli; He, Yong; Qiu, Zhengjun; Wu, Di

    2008-03-01

    This research aimed for development multi-spectral imaging technique for green tea categories discrimination based on texture analysis. Three key wavelengths of 550, 650 and 800 nm were implemented in a common-aperture multi-spectral charged coupled device camera, and images were acquired for 190 unique images in a four different kinds of green tea data set. An image data set consisting of 15 texture features for each image was generated based on texture analysis techniques including grey level co-occurrence method (GLCM) and texture filtering. For optimization the texture features, 5 features that weren't correlated with the category of tea were eliminated. Unsupervised cluster analysis was conducted using the optimized texture features based on principal component analysis. The cluster analysis showed that the four kinds of green tea could be separated in the first two principal components space, however there was overlapping phenomenon among the different kinds of green tea. To enhance the performance of discrimination, least squares support vector machine (LSSVM) classifier was developed based on the optimized texture features. The excellent discrimination performance for sample in prediction set was obtained with 100%, 100%, 75% and 100% for four kinds of green tea respectively. It can be concluded that texture discrimination of green tea categories based on multi-spectral image technology is feasible.

  1. An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2013-01-01

    Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.

  2. SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

    Science.gov (United States)

    Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

    2013-03-01

    Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors.

  3. [Study on application of SVM in prediction of coronary heart disease].

    Science.gov (United States)

    Zhu, Yue; Wu, Jianghua; Fang, Ying

    2013-12-01

    Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

  4. Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition

    Directory of Open Access Journals (Sweden)

    Sandhya Arora

    2010-05-01

    Full Text Available Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM, multiple classifier combination, etc. In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters. After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features. These features are then fed to Neural classifier and in support vector machine for classification. In neural classifier, we explored three ways of combining decisions of four MLP's, designed for four different features.

  5. Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition

    CERN Document Server

    Arora, Sandhya; Nasipuri, Mita; Malik, L; Kundu, M; Basu, D K

    2010-01-01

    Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters. After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features. These features are then fed to Neural classifier and in support vector machine for classification. In neural classifier, we explored three ways of combining decisions of four MLP's designed for four different features.

  6. An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

    Directory of Open Access Journals (Sweden)

    Yinan Zhang

    2016-01-01

    Full Text Available Diabetic retinopathy (DR screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.

  7. Classifier in Age classification

    Directory of Open Access Journals (Sweden)

    B. Santhi

    2012-12-01

    Full Text Available Face is the important feature of the human beings. We can derive various properties of a human by analyzing the face. The objective of the study is to design a classifier for age using facial images. Age classification is essential in many applications like crime detection, employment and face detection. The proposed algorithm contains four phases: preprocessing, feature extraction, feature selection and classification. The classification employs two class labels namely child and Old. This study addresses the limitations in the existing classifiers, as it uses the Grey Level Co-occurrence Matrix (GLCM for feature extraction and Support Vector Machine (SVM for classification. This improves the accuracy of the classification as it outperforms the existing methods.

  8. Comparison and Retrieval of Liver Diseases Based on the Performance of SVM and SOM

    Directory of Open Access Journals (Sweden)

    R. Suganya

    2012-12-01

    Full Text Available In this study, we distinguish the liver tumor by SVM and SOM classification. LPND (Laplacian Pyramid based Nonlinear DiTusion is the proposed speckle reduction technique for preprocessing the image. In Feature extraction, we segment the image based on mean, variance, entropy and fractal dimension. The four layer hierarchical scheme is used for classifying benign and malignant tumors. In the Wrst layer the normal tissue distinguishes from abnormal tissues. The second layer distinguishes cyst from abnormal tissues. Cavernous Hemangioma is identiWed in third layer. At last hepatoma is identiWed from undeWned tissues. Self Organizing Map (SOM and Support Vector Machine (SVM algorithms are used to classify the features extracted from liver diseases. Using performance metrics such as sensitivity and specificity, our results demonstrate that the SVM provide better retrieval than SOM.

  9. Face recognition with multi-kernel SVM%基于多核SVM的人脸识别

    Institute of Scientific and Technical Information of China (English)

    陆萍

    2016-01-01

    Support Vector Machine (SVM) is one of the most importance linear classifier in machine learning, which can classify the non-linear samples efficiently via the kernel method. However, the accuracy of SVM may be heavily affected due to the characteristics of different kernels. To make better use of different kernels, the different kernels are tried to fuse to design a multi-kernel SVM, and the resulting classifier is evaluated on the ORL and AR face recognition datasets. As for the feature, the Local Ternary Pattern (LTP) is employed. The experimental results show that the multi-kernel SVM can achieve higher classification accuracy than traditional SVM with single kernel.%支持向量机(Support Vector Machine, SVM)是机器学习领域中非常重要的一种线性分类器,借助于核方法, SVM能够实现对非线性样本的有效分类。但是不同类别的核函数具有各自不同的特性,对于SVM分类的准确率也具有很大的影响。为了能够结合不同核函数的优势,本文采用了对不同核函数进行融合的方式来设计多核SVM分类器,并在ORL与AR人脸识别数据集上采用局部三值模式(Local Ternary Pattern, LTP)作为特征描述子进行了验证。实验结果表明,多核SVM比使用普通核函数的SVM具有更优的分类准确率。

  10. PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    Sari Ayu Wulandari

    2016-06-01

    Full Text Available Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w, dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means, variasi Varians, skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri  PCA (Principle Com­­ponent Analysis. Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent, PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks.

  11. 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.

  12. Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.

    Science.gov (United States)

    Goldbaum, Michael H; Lee, Intae; Jang, Giljin; Balasubramanian, Madhusudhanan; Sample, Pamela A; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Anderson, Douglas R; Zangwill, Linda M; Fredette, Marie-Josee; Jung, Tzyy-Ping; Medeiros, Felipe A; Bowd, Christopher

    2012-09-25

    We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.

  13. DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

    Science.gov (United States)

    Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

    2016-12-08

    In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

  14. Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

    Science.gov (United States)

    Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

    2010-12-01

    We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

  15. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  16. Automatic Language Identification with Discriminative Language Characterization Based on SVM

    Science.gov (United States)

    Suo, Hongbin; Li, Ming; Lu, Ping; Yan, Yonghong

    Robust automatic language identification (LID) is the task of identifying the language from a short utterance spoken by an unknown speaker. The mainstream approaches include parallel phone recognition language modeling (PPRLM), support vector machine (SVM) and the general Gaussian mixture models (GMMs). These systems map the cepstral features of spoken utterances into high level scores by classifiers. In this paper, in order to increase the dimension of the score vector and alleviate the inter-speaker variability within the same language, multiple data groups based on supervised speaker clustering are employed to generate the discriminative language characterization score vectors (DLCSV). The back-end SVM classifiers are used to model the probability distribution of each target language in the DLCSV space. Finally, the output scores of back-end classifiers are calibrated by a pair-wise posterior probability estimation (PPPE) algorithm. The proposed language identification frameworks are evaluated on 2003 NIST Language Recognition Evaluation (LRE) databases and the experiments show that the system described in this paper produces comparable results to the existing systems. Especially, the SVM framework achieves an equal error rate (EER) of 4.0% in the 30-second task and outperforms the state-of-art systems by more than 30% relative error reduction. Besides, the performances of proposed PPRLM and GMMs algorithms achieve an EER of 5.1% and 5.0% respectively.

  17. Tree Crown Delineation on Vhr Aerial Imagery with Svm Classification Technique Optimized by Taguchi Method: a Case Study in Zagros Woodlands

    Science.gov (United States)

    Erfanifard, Y.; Behnia, N.; Moosavi, V.

    2013-09-01

    The Support Vector Machine (SVM) is a theoretically superior machine learning methodology with great results in classification of remotely sensed datasets. Determination of optimal parameters applied in SVM is still vague to some scientists. In this research, it is suggested to use the Taguchi method to optimize these parameters. The objective of this study was to detect tree crowns on very high resolution (VHR) aerial imagery in Zagros woodlands by SVM optimized by Taguchi method. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The VHR aerial imagery of the plot with 0.06 m spatial resolution was obtained from National Geographic Organization (NGO), Iran, to extract the crowns of Persian oak trees in this study. The SVM parameters were optimized by Taguchi method and thereafter, the imagery was classified by the SVM with optimal parameters. The results showed that the Taguchi method is a very useful approach to optimize the combination of parameters of SVM. It was also concluded that the SVM method could detect the tree crowns with a KHAT coefficient of 0.961 which showed a great agreement with the observed samples and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimize the parameters of classification techniques like SVM.

  18. A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

    Directory of Open Access Journals (Sweden)

    Prasertsak Charoen

    2017-04-01

    Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

  19. Mandarin Digits Speech Recognition Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    XIE Xiang; KUANG Jing-ming

    2005-01-01

    A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33%, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.

  20. Sales Growth Rate Forecasting Using Improved PSO and SVM

    Directory of Open Access Journals (Sweden)

    Xibin Wang

    2014-01-01

    Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

  1. Anomaly Detection of Ranging Information of Airborne TACAN Based on One-Class SVM Classifier%基于One-Class SVM的机载塔康测距信息异常检测方法研究

    Institute of Scientific and Technical Information of China (English)

    李城梁

    2015-01-01

    针对多源导航信息融合系统中导航传感器数据保障的问题,本文提出了一种基于One-Class SVM的机载塔康测距信息异常检测方法。首先,提取机载塔康测距信息的时域参数构成特征样本空间;然后,采用One-Class SVM训练出机载塔康测距信息正常状态时的模型,通过发现非正常状态的样本进行异常检测。利用模拟的机载塔康测距数据进行方法验证,实验结果表明:该异常检测方法对机载塔康测距信息中的噪声有一定的鲁棒性,可以满足实际应用的需要。%According to the anomaly detection for ranging information of airborne TACAN in system which fused information of multi-source navigation, this article proposed a detection method based on One-Class SVM. First, the features space is built by extracting time domain features. Then, the model of normal state is built by training the One-Class SVM classifier. Anomaly information is detected by the trained model. The experimental results show that the proposed method has good performance in anomaly detection.

  2. A DSRPCL-SVM Approach to Informative Gene Analysis

    Institute of Scientific and Technical Information of China (English)

    Wei Xiong; Zhibin Cai; Jinwen Ma

    2008-01-01

    Microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning (DSRPCL) algorithm and then detect the informative gene cluster or set with the help of support vector machine (SVM). Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis.

  3. Intrusion Awareness Based on Data Fusion and SVM Classification

    Directory of Open Access Journals (Sweden)

    Ramnaresh Sharma

    2012-06-01

    Full Text Available Network intrusion awareness is important factor for risk analysis of network security. In the current decade various method and framework are available for intrusion detection and security awareness. Some method based on knowledge discovery process and some framework based on neural network. These entire model take rule based decision for the generation of security alerts. In this paper we proposed a novel method for intrusion awareness using data fusion and SVM classification. Data fusion work on the biases of features gathering of event. Support vector machine is super classifier of data. Here we used SVM for the detection of closed item of ruled based technique. Our proposed method simulate on KDD1999 DARPA data set and get better empirical evaluation result in comparison of rule based technique and neural network model.

  4. Intrusion Awareness Based on Data Fusion and SVM Classification

    Directory of Open Access Journals (Sweden)

    Ramnaresh Sharma

    2012-06-01

    Full Text Available Network intrusion awareness is important factor forrisk analysis of network security. In the currentdecade various method and framework are availablefor intrusion detection and security awareness.Some method based on knowledge discovery processand some framework based on neural network.These entire model take rule based decision for thegeneration of security alerts. In this paper weproposed a novel method for intrusion awarenessusing data fusion and SVM classification. Datafusion work on the biases of features gathering ofevent. Support vector machine is super classifier ofdata. Here we used SVM for the detection of closeditem of ruled based technique. Our proposedmethod simulate on KDD1999 DARPA data set andget better empirical evaluation result in comparisonof rule based technique and neural network model.

  5. Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classification

    Institute of Scientific and Technical Information of China (English)

    Kun Tan; Peijun Du

    2011-01-01

    @@ Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer Ⅱ hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy (97.06%). Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.%Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer Ⅱ hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four

  6. 基于 SVM 的便携式睡眠监测系统设计%A design of sleep monitoring system based on support vector machines

    Institute of Scientific and Technical Information of China (English)

    林秀晶; 钱松荣

    2015-01-01

    Objective Sleep monitoring is an important part of the analysis of sleep quality , yet the sleep monitoring system available now is complex and cumbersome .A portable sleep monitoring system based on support vector machines ( SVM) is proposed in this paper with great convenience and efficiency .Methods The system’ s hardware consists of the server and the user equipment .The user equipment with high portability is used for data acquisition and data transmission . The server is used for data analysis and resource maintenance.SVM is adopted as the automatic sleep analysis algorithm in the server .Based on extracted features, sleep stages are got with directed acyclic graph as the multi-classification method.Results The research results based on patient EEG analysis show that the system can reach a high accuracy rate and take short analysis time average analysis time of 1.45 seconds.Conclusions The compact user equipment is highly portable , and it can feedback the correct result to the users in real time , thus confirming that the design has a promising future in sleep monitoring .%目的:睡眠监测是睡眠质量分析中重要的环节,但目前的睡眠监测系统复杂而且难以携带。本文提出基于支持向量机的便携式睡眠监测系统,以方便地实时监控睡眠。方法该系统硬件部分由服务器和用户端设备构成,其中用户端设备负责数据采集和数据传输,服务器端负责数据分析及相关的资源管理。睡眠分析软件采用支持向量机( support vector machines , SVM)作为分析算法,在提取特征值的基础上,以有向无环图作为多分类策略分析得到睡眠的时相。结果对于患者的睡眠脑电实验表明分析正确率高,所需的分析时间短。结论该系统用户端设备体积小,方便携带,分析正确率高,实时性好,在睡眠监测领域具有良好的应用前景。

  7. Support Vector Machine for mechanical faults classification

    Institute of Scientific and Technical Information of China (English)

    JIANG Zhi-qiang; FU Han-guang; LI Ling-jun

    2005-01-01

    Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for online diagnosis for mechanical system.

  8. Support vector machine used to diagnose the fault of rotor broken bars of induction motors

    DEFF Research Database (Denmark)

    Zhitong, Cao; Jiazhong, Fang; Hongpingn, Chen

    2003-01-01

    The data-based machine learning is an important aspect of modern intelligent technology, while statistical learning theory (SLT) is a new tool that studies the machine learning methods in the case of a small number of samples. As a common learning method, support vector machine (SVM) is derived...... for the SVM. After a SVM is trained with learning sample vectors, so each kind of the rotor broken bar faults of induction motors can be classified. Finally the retest is demonstrated, which proves that the SVM really has preferable ability of classification. In this paper we tried applying the SVM...... from the SLT. Here we were done some analogical experiments of the rotor broken bar faults of induction motors used, analyzed the signals of the sample currents with Fourier transform, and constructed the spectrum characteristics from low frequency to high frequency used as learning sample vectors...

  9. Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

    Science.gov (United States)

    Ornostay, Anna; Cowie, Andrew M; Hindle, Matthew; Baker, Christopher J O; Martyniuk, Christopher J

    2013-12-01

    The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species.

  10. A NEW SVM BASED EMOTIONAL CLASSIFICATION OF IMAGE

    Institute of Scientific and Technical Information of China (English)

    Wang Weining; Yu Yinglin; Zhang Jianchao

    2005-01-01

    How high-level emotional representation of art paintings can be inferred from percep tual level features suited for the particular classes (dynamic vs. static classification)is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.

  11. Pavement Crack Classifiers: A Comparative Study

    Directory of Open Access Journals (Sweden)

    S. Siddharth

    2012-12-01

    Full Text Available Non Destructive Testing (NDT is an analysis technique used to inspect metal sheets and components without harming the product. NDT do not cause any change after inspection; this technique saves money and time in product evaluation, research and troubleshooting. In this study the objective is to perform NDT using soft computing techniques. Digital images are taken; Gray Level Co-occurrence Matrix (GLCM extracts features from these images. Extracted features are then fed into the classifiers which classifies them into images with and without cracks. Three major classifiers: Neural networks, Support Vector Machine (SVM and Linear classifiers are taken for the classification purpose. Performances of these classifiers are assessed and the best classifier for the given data is chosen.

  12. Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example

    Directory of Open Access Journals (Sweden)

    Pegah Kassraian Fard

    2016-12-01

    Full Text Available Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large inter-individual differences. Typically the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multi-disciplinary team with extensive experience. Whilst the application of machine learning classification methods (ML classifiers to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are not currently opaque and accessible to researchers and clinicians outside the field. In this paper, we describe potential classification pipelines for Autism Spectrum Disorder, as an example of a psychiatric disorder. The analyses are based on resting-state fMRI data derived from a multi-site data repository (ABIDE. We compare several popular ML classifiers such as support vector machines, neural networks and regression approaches, among others. In a tutorial style, written to be equally accessible for researchers and clinicians, we explain the rationale of each classification approach, clarify the underlying assumptions, and discuss possible pitfalls and challenges. We also provide the data as well as the MATLAB code we used to achieve our results. We show that out-of-the-box ML classifiers can yield classification accuracies of about 60-70%. Finally, we discuss how classification accuracy can be further improved, and we mention methodological developments that are needed to pave the way for the use of ML classifiers in clinical practice.

  13. Object-Based Image Classification of Summer Crops with Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    José M. Peña

    2014-05-01

    Full Text Available The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR, support vector machine (SVM and multilayer perceptron (MLP neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86% and notably higher than C4.5 (79%. The SVM+SVM classifier (best method improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity. The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

  14. Relationship Between Support Vector Set and Kernel Functions in SVM

    Institute of Scientific and Technical Information of China (English)

    张铃; 张钹

    2002-01-01

    Based on a constructive learning approach, covering algorithms, we investigatethe relationship between support vector sets and kernel functions in support vector machines(SVM). An interesting result is obtained. That is, in the linearly non-separable case, any sampleof a given sample set K can become a support vector under a certain kernel function. The resultshows that when the sample set K is linearly non-separable, although the chosen kernel functionsatisfies Mercer's condition its corresponding support vector set is not necessarily the subsetof K that plays a crucial role in classifying K. For a given sample set, what is the subsetthat plays the crucial role in classification? In order to explore the problem, a new concept,boundary or boundary points, is defined and its properties are discussed. Given a sample setK, we show that the decision functions for classifying the boundary points of K are the sameas that for classifying the K itself. And the boundary points of K only depend on K and thestructure of the space at which K is located and independent of the chosen approach for findingthe boundary. Therefore, the boundary point set may become the subset of K that plays acrucial role in classification. These results are of importance to understand the principle of thesupport vector machine (SVM) and to develop new learning algorithms.

  15. Less is More: Data Processing with SVM for Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

    XIAO Hai-jun; HONG Fan; WANG Ling

    2009-01-01

    To improve the detection rate and lower down the false positive rate in intrusion detection system,dimensionality reduction is widely used in the intrusion detection system.For this purpose,a data processing (DP) with support vector machine (SVM) was built.Different from traditionally identifying the redundant data before purging the audit data by expert knowledge or utilizing different kinds of subsets of the available 41-connection attributes to build a classifier,the proposed strategy first removes the attributes whose correlation with another attribute exceeds a threshold,and then classifies two sequence samples as one class while removing either of the two samples whose similarity exceeds a threshold.The results of performance experiments showed that the strategy of DP and SVM is superior to the other existing data reduction strategies (e.g.,audit reduction,rule extraction,and feature selection),and that the detection model based on DP and SVM outperforms those based on data mining,soft computing,and hierarchical principal component analysis neural networks.

  16. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

    Science.gov (United States)

    Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

    2017-03-15

    Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (Pdoctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback

  17. Classifier of intestinal contractile activity degree based on internal electroenterogram recording.

    Science.gov (United States)

    Guimera-Tomas, J; Ye-Lin, Y; Garcia-Casado, J; Prats-Boluda, G

    2010-01-01

    The study of the intestinal interdigestive motor migratory complex (IMMC) is relevant in gastroenterology because most of the gastrointestinal pathologies are reflected in anomalies of the IMMC. The aim of this work is to develop an automatic classifier to discriminate among the different intestinal contractile activity degrees (quiescence, irregular, and maximum contractile activity) that compound the IMMC from the internal recordings of electroenterogram. Spectral and statistical parameters estimated from the internal electroenterogram have been used as features to the classifiers based on Linear Discriminant Analysis (LDA) and linear Support Vector Machines (SVM). The accuracy obtained by the SVM classifier is slightly higher than that of the LDA classifier. An accuracy of around 91% was obtained for the binary SVM classifier (quiescence vs maximum activity) and around 74% for the multiclass one. The use of additional features, and non-linear SVM classifiers could yield better classification accuracy values. Nevertheless, preliminary results suggest that SVM classifiers could be a very helpful tool for automatic classification of intestinal contractile activity degrees and for the identification of the IMMC which could be used for diagnosing anomalies in the intestinal motor function.

  18. A Hybrid Framework using RBF and SVM for Direct Marketing

    Directory of Open Access Journals (Sweden)

    M. Govidarajan

    2013-05-01

    Full Text Available one of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for direct marketing. Direct marketing has become an important application field for data mining. In direct marketing, companies or organizations try to establish and maintain a direct relationship with their customers in order to target them individually for specific product offers or for fund raising. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. In this research work, new hybrid classification method is proposed by combining classifiers in a heterogeneous environment using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF and Support Vector Machine (SVM as base classifiers. Here, modified training sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. Empirical results illustrate that the proposed hybrid systems provide more accurate direct marketing system.

  19. A SVM-based method for sentiment analysis in Persian language

    Science.gov (United States)

    Hajmohammadi, Mohammad Sadegh; Ibrahim, Roliana

    2013-03-01

    Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

  20. [Application of SVM and wavelet analysis in EEG classification].

    Science.gov (United States)

    Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei

    2011-04-01

    We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.

  1. Using machine learning to classify image features from canine pelvic radiographs

    DEFF Research Database (Denmark)

    McEvoy, Fintan; Amigo Rubio, Jose Manuel

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine...

  2. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  3. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  4. Protein–Protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM

    Indian Academy of Sciences (India)

    Brijesh Kumar Sriwastava; Subhadip Basu; Ujjwal Maulik

    2015-10-01

    Protein–protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  5. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

    Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  6. SVM for Solving Forward Problems of EIT.

    Science.gov (United States)

    Wu, Youxi; Li, Ying; Guo, Lei; Yan, Weili; Shen, Xueqin; Fu, Kun

    2005-01-01

    Support Vector Machine (SVM) can be seen as a new machine learning way which is based on the idea of VC dimensions and the principle of structural risk minimization rather than empirical risk minimization. SVM can be used for classification and regression. Support Vector Regression (SVR) is a very important branch of Support Vector Machine. Partial Differential Equations (PDEs) have been successfully treated by using SVR in previous works. The forward problems of EIT are the basis of EIT inverse problems. The forward problem's essence is to solve PDEs. The method has been successfully tested on the forward problems of EIT and has yielded accurate results.

  7. SVM CLASSIFICATION:ITS CONTENTS AND CHALLENGES

    Institute of Scientific and Technical Information of China (English)

    YueShihong; LiPing; HaoPeiyi

    2003-01-01

    SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. In particular,they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically. There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation. In this paper,survey ofthe key contents on this subject, focusing on the most well-known models based on kernel substitution, namely SVM, as well as the activated fields at present and the development tendency,is presented.

  8. Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

    Science.gov (United States)

    Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

    2016-01-01

    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

  9. Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

    Science.gov (United States)

    2016-01-01

    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

  10. Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

    Directory of Open Access Journals (Sweden)

    Fabrício R. Silva

    2013-06-01

    Full Text Available PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs for glaucoma diagnosis using Spectral Domain OCT (SD-OCT and standard automated perimetry (SAP. METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP and retinal nerve fiber layer (RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California. Receiver operating characteristic (ROC curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG, Naive-Bayes (NB, Multilayer Perceptron (MLP, Radial Basis Function (RBF, Random Forest (RAN, Ensemble Selection (ENS, Classification Tree (CTREE, Ada Boost M1(ADA,Support Vector Machine Linear (SVML and Support Vector Machine Gaussian (SVMG. Areas under the receiver operating characteristic curves (aROC obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE to 0.946 (RAN.The best OCT+SAP aROC obtained with RAN (0.946 was significantly larger the best single OCT parameter (p<0.05, but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19. CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

  11. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  12. SVM multiuser detection based on heuristic kernel

    Institute of Scientific and Technical Information of China (English)

    Yang Tao; Hu Bo

    2007-01-01

    A support vector machine (SVM) based multiuser detection (MUD) scheme in code-division multiple-access (CDMA) system is proposed. In this scheme, the equivalent support vector (SV) is obtained through a kernel sparsity approximation algorithm, which avoids the conventional costly quadratic programming (QP) procedure in SVM. Besides, the coefficient of the SV is attained through the solution to a generalized eigenproblem. Simulation results show that the proposed scheme has almost the same bit error rate (BER) as the standard SVM and is better than minimum mean square error (MMSE) scheme. Meanwhile, it has a low computation complexity.

  13. Application of machine learning on brain cancer multiclass classification

    Science.gov (United States)

    Panca, V.; Rustam, Z.

    2017-07-01

    Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

  14. Reducing Support Vector Machine Classification Error by Implementing Kalman Filter

    Directory of Open Access Journals (Sweden)

    Muhsin Hassan

    2013-08-01

    Full Text Available The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques.

  15. Facial Expression Recognition Using SVM Classifier

    OpenAIRE

    2015-01-01

    Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels...

  16. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

  17. 线性支持向量机多类分类器几何构造方法%Geometric Construction Method of Linear SVM Multi-class Classifier

    Institute of Scientific and Technical Information of China (English)

    唐英; 李应珍

    2012-01-01

    针对支持向量机多类分类问题,根据样本点集凸包找寻模式类间隙,通过提取模式类间隙多边形中轴线构造多类分类边界.当基本支持向量机扩展为多类分类问题时,该方法克服了OAO和OAA等传统方法存在的决策盲区和类别不平衡等缺陷.基于仿真数据集的实验结果表明,构造的分类边界在保证分类精度的同时,能够使分类空隙最大化,实现对线性可分多类数据的最优分类.%A new method to construct multi-class classifier based on linear SVM is proposed in the paper. Its major procedures include: to form interval space polygon among point sets by subtracting operation of convex hulls, to extract polygon axes and then extend to construct the classification boundaries. The method can avoid problems like blind area in decision-making and imbalance data sets like traditional multi-class classification ways of One-Against-All(OAA) and One-Agianst-One(OAO) encounter, Simulation test results show that classification boundaries constructed by the method can realize the minimum risk and the maximum interval space among point sets, thus can be seen as an embodiment of the optimal classification lines of multi-class point sets.

  18. EHPred: an SVM-based method for epoxide hydrolases recognition and classification

    Institute of Scientific and Technical Information of China (English)

    JIA Jia; YANG Liang; ZHANG Zi-zhang

    2006-01-01

    A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM classifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew,s correlation coefficient (MCC) of 0.84.Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.

  19. B-FlaP: Classifying Gamma-ray Blazars Using Machine Learning

    Science.gov (United States)

    Thompson, David John; Chiaro, Graziano; Giroletti, Marcello; Salvetti, David; La Mura, Giovanni; Bastieri, Denis

    2017-01-01

    In the Third Fermi Large Area Telescope Catalog of high-energy gamma-ray sources, 573 are listed as Blazar Candidates of Uncertain type (BCU), or sources without a conclusive classification. Blazar Flaring Patterns (B-FlaP) uses Empirical Cumulative Distribution Function and Artificial Neural Network machine-learning techniques for a fast method of screening and classification of BCUs based on gamma-ray data only, when rigorous multiwavelength analysis is not available. In this study radio analysis and direct observations by ground-based optical observatories are used to validate the B-FlaP method. Tests indicate that the method is effective, suggesting that 342 sources are likely BL Lac objects, 154 are likely Flat Spectrum Radio Quasars, with only 77 remaining uncertain. 53 of the BCUs appear to be High Synchrotron Peaked blazars, a class of particular interest to ground-based imaging atmospheric Cherenkov telescopes.

  20. Incremental support vector machines for fast reliable image recognition

    Energy Technology Data Exchange (ETDEWEB)

    Makili, L., E-mail: makili_le@yahoo.com [Instituto Superior Politécnico da Universidade Katyavala Bwila, Benguela (Angola); Vega, J. [Asociación EURATOM/CIEMAT para Fusión, Madrid (Spain); Dormido-Canto, S. [Dpto. Informática y Automática – UNED, Madrid (Spain)

    2013-10-15

    Highlights: ► A conformal predictor using SVM as the underlying algorithm was implemented. ► It was applied to image recognition in the TJ–II's Thomson Scattering Diagnostic. ► To improve time efficiency an approach to incremental SVM training has been used. ► Accuracy is similar to the one reached when standard SVM is used. ► Computational time saving is significant for large training sets. -- Abstract: This paper addresses the reliable classification of images in a 5-class problem. To this end, an automatic recognition system, based on conformal predictors and using Support Vector Machines (SVM) as the underlying algorithm has been developed and applied to the recognition of images in the Thomson Scattering Diagnostic of the TJ–II fusion device. Using such conformal predictor based classifier is a computationally intensive task since it implies to train several SVM models to classify a single example and to perform this training from scratch takes a significant amount of time. In order to improve the classification time efficiency, an approach to the incremental training of SVM has been used as the underlying algorithm. Experimental results show that the overall performance of the new classifier is high, comparable to the one corresponding to the use of standard SVM as the underlying algorithm and there is a significant improvement in time efficiency.

  1. 基于支持向量机无限集成学习方法的遥感图像分类%Remotely sensed imagery classification by SVM-based Infinite Ensemble Learning method

    Institute of Scientific and Technical Information of China (English)

    杨娜; 秦志远; 张俊

    2013-01-01

    基于支持向量机的无限集成学习方法(SVM-based IEL)是机器学习领域新兴起的一种集成学习方法.本文将SVM-based IEL引入遥感图像的分类领域,并同时将SVM、Bagging、AdaBoost和SVM-based IEL等方法应用于遥感图像分类.实验表明:Bagging方法可以提高遥感图像的分类精度,而AdaBoost却降低了遥感图像的分类精度;同时,与SVM、有限集成的学习方法相比,SVM-based IEL方法具有可以显著地提高遥感图像的分类精度、分类效率的优势.%Support-vector-machines-based Infinite Ensemble Learning method ( SVM-based IEL) is one of the ensemble learning methods in the field of machine learning. In this paper, the SVM-based IEL was applied to the classification of remotely sensed imagery besides classic ensemble learning methods such as Bagging, AdaBoost and SVM etc. SVM was taken as the base classifier in Bagging, AdaBoost The experiments showed that the classic ensemble learning methods have different performances compared to SVM. In detail , the Bagging was capable of enhancing the classification accuracy but the AdaBoost was decreasing the classification accuracy. Furthermore, the experiments suggested that compared to SVM and classic ensemble learning methods, SVM-based IEL has many merits such as increasing both of the classification accuracy and classification efficiency.

  2. Density-based penalty parameter optimization on C-SVM.

    Science.gov (United States)

    Liu, Yun; Lian, Jie; Bartolacci, Michael R; Zeng, Qing-An

    2014-01-01

    The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

  3. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Directory of Open Access Journals (Sweden)

    Fei Zhu

    Full Text Available Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs and conditional random fields (CRFs, which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1 of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  4. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Science.gov (United States)

    Zhu, Fei; Shen, Bairong

    2012-01-01

    Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1) of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  5. Quantum-inspired evolutionary tuning of SVM parameters

    Institute of Scientific and Technical Information of China (English)

    Zhiyong Luo; Ping Wang; Yinguo Li; Wenfeng Zhang; Wei Tang; Min Xiang

    2008-01-01

    The most commonly used parameters selection method for support vector machines (SVM) is cross-validation, which needs a longtime complicated calculation. In this paper, a novel regularization parameter and a kernel parameter tuning approach of SVM are presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and the improved LS-SVM with wavelet kernel can provide better precision.

  6. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    Science.gov (United States)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  7. Data fusion and machine learning to identify threat vectors for the Zika virus and classify vulnerability

    Science.gov (United States)

    Gentle, J. N., Jr.; Kahn, A.; Pierce, S. A.; Wang, S.; Wade, C.; Moran, S.

    2016-12-01

    With the continued spread of the zika virus in the United States in both Florida and Virginia, increased public awareness, prevention and targeted prediction is necessary to effectively mitigate further infection and propagation of the virus throughout the human population. The goal of this project is to utilize publicly accessible data and HPC resources coupled with machine learning algorithms to identify potential threat vectors for the spread of the zika virus in Texas, the United States and globally by correlating available zika case data collected from incident reports in medical databases (e.g., CDC, Florida Department of Health) with known bodies of water in various earth science databases (e.g., USGS NAQWA Data, NASA ASTER Data, TWDB Data) and by using known mosquito population centers as a proxy for trends in population distribution (e.g., WHO, European CDC, Texas Data) while correlating historical trends in the spread of other mosquito borne diseases (e.g., chikungunya, malaria, dengue, yellow fever, west nile, etc.). The resulting analysis should refine the identification of the specific threat vectors for the spread of the virus which will correspondingly increase the effectiveness of the limited resources allocated towards combating the disease through better strategic implementation of defense measures. The minimal outcome of this research is a better understanding of the factors involved in the spread of the zika virus, with the greater potential to save additional lives through more effective resource utilization and public outreach.

  8. Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences

    OpenAIRE

    M.L. Khodra; D.H. Widyantoro; E.A. Aziz; B.R. Trilaksono

    2011-01-01

    This research employs free model that uses only sentential features without paragraph context to extract topic sentences of a paragraph. For finding optimal combination of features, corpus-based classification is used for constructing a sentence classifier as the model. The sentence classifier is trained by using Support Vector Machine (SVM). The experiment shows that position and meta-discourse features are more important than syntactic features to extract topic sentence, and the best perfor...

  9. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

    Science.gov (United States)

    Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

    2016-09-02

    Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

  10. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

    Directory of Open Access Journals (Sweden)

    Yi Long

    2016-09-01

    Full Text Available Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM optimized by particle swarm optimization (PSO to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz, a three-layer wavelet packet analysis (WPA is used for feature extraction, after which, the kernel principal component analysis (kPCA is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

  11. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

    Science.gov (United States)

    Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

    2016-01-01

    Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance. PMID:27598160

  12. Automatic Parameters Selection for SVM Based on PSO

    Institute of Scientific and Technical Information of China (English)

    ZHANG Mingfeng; ZHU Yinghua; ZHENG Xu; LIU Yu

    2007-01-01

    Motivated by the fact that automatic parameters selection for Support Vector Machine (SVM) is an important issue to make SVM practically useful and the common used Leave-One-Out (LOO) method is complex calculation and time consuming,an effective strategy for automatic parameters selection for SVM is proposed by using the Particle Swarm Optimization (PSO) in this paper.Simulation results of practice data model demonstrate the effectiveness and high efficiency of the proposed approach.

  13. DDoS detection based on wavelet kernel support vector machine

    Institute of Scientific and Technical Information of China (English)

    YANG Ming-hui; WANG Ru-chuan

    2008-01-01

    To enhance the detection accuracy and deduce false positive rate of distributed denial of service (DDoS) attack detection, a new machine learning method was proposed. With the analysis of support vector machine (SVM) and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine (WSVM) is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.

  14. A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

    Institute of Scientific and Technical Information of China (English)

    CAO Shu-gang; LIU Yan-bao; WANG Yan-ping

    2008-01-01

    To improve the precision and reliability in predicting methane hazard in working face of coal mine, we have proposed a forecasting and forewarning model for methane hazard based on the least square support vector (LS-SVM) multi-classifier and regression machine. For the forecasting model, the methane concentration can be considered as a nonlinear time series and the time series analysis method is adopted to predict the change in methane concentration using LS-SVM regression. For the forewarning model, which is based on the forecasting results, by the multi-classification method of LS-SVM, the methane hazard was identified to four grades: normal, attention, warning and danger. According to the forewarning results, corresponding measures are taken. The model was used to forecast and forewarn the K9 working face. The results obtained by LS-SVM regression show that the forecast- ing have a high precision and forewarning results based on a LS-SVM multi-classifier are credible. Therefore, it is an effective model building method for continuous prediction of methane concentration and hazard forewarning in working face.

  15. Support vector machine as an alternative method for lithology classification of crystalline rocks

    Science.gov (United States)

    Deng, Chengxiang; Pan, Heping; Fang, Sinan; Amara Konaté, Ahmed; Qin, Ruidong

    2017-03-01

    With the expansion of machine learning algorithms, automatic lithology classification that uses well logging data is becoming significant in formation evaluation and reservoir characterization. In fact, the complicated composition and structural variations of metamorphic rocks result in more nonlinear features in well logging data and elevate requirements to algorithms. Herein, the application of the support vector machine (SVM) in classifying crystalline rocks from Chinese Continental Scientific Drilling Main Hole (CCSD-MH) data was reported. We found that the SVM performs poorly on the lithology classification of crystalline rocks when training samples are imbalanced. The fact is that training samples are generally limited and imbalanced as cores cannot be obtained balanced and at 100 percent. In this paper, we introduced the synthetic minority over-sampling technique (SMOTE) and Borderline-SMOTE to deal with imbalanced data. After experiments generating different quantities of training samples by SMOTE and Borderline-SMOTE, the most suitable classifier was selected to overcome the disadvantage of the SVM. Then, the popular supervised classifier back-propagation neural networks (BPNN), which has been proved competent for lithology classification of crystalline rocks in previous studies, was compared to evaluate the performance of the SVM. Results show that Borderline-SMOTE can improve the SVM with substantially increased accuracy even for minority classes in a reasonable manner, while the SVM outperforms BPNN in aspects of lithology prediction and CCSD-MH data generalization. We demonstrate the potential of the SVM as an alternative to current methods for lithology identification of crystalline rocks.

  16. Optical diagnosis of colon and cervical cancer by support vector machine

    Science.gov (United States)

    Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Dey, Rajib; Das, Nandan K.; Pradhan, Sanjay; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.; Mohanty, Samarendra

    2016-05-01

    A probabilistic robust diagnostic algorithm is very much essential for successful cancer diagnosis by optical spectroscopy. We report here support vector machine (SVM) classification to better discriminate the colon and cervical cancer tissues from normal tissues based on elastic scattering spectroscopy. The efficacy of SVM based classification with different kernel has been tested on multifractal parameters like Hurst exponent, singularity spectrum width in order to classify the cancer tissues.

  17. Vibration fault diagnosis for steam turbine by using support vector machine based on fruit fly optimization algorithm%基于 FOA -SVM 的汽轮机振动故障诊断

    Institute of Scientific and Technical Information of China (English)

    石志标; 苗莹

    2014-01-01

    为解决支持向量机算法(Support Vector Machine,SVM)的核函数参数及惩罚因子参数选取的盲目性,利用果蝇优化算法(Fruit Fly Optimization Algorithm,FOA)对 SVM中参数进行优化。提出基于 FOA 的 SVM故障诊断算法,并对汽轮机故障实验数据进行模式识别。该算法能对 SVM相关参数自动寻优,且能达到较理想的全局最优解。通过与常用的粒子群算法(Particle Swarm Optimization,PSO)与遗传算法(Genetic Algorithm,GA)优化后支持向量机进行对比。结果表明,FOA -SVM算法稳定、识别速度快、识别率高。%In order to solve the problem that the selection of the kernel function parameters and penalty factor parameters in the support vector machine(SVM)algorithm is blindfold,the fruit fly optimization algorithm (FOA)was applied to optimize the parameters in SVM.A fault diagnosis algorithm of SVM based on FOA was put forward,and then the pattern recognition of experimental turbine failure data was performed.The algorithm can optimize the SVMparameters automatically,and achieve ideal global optimal solution.Comparing with the SVMwhich is optimized by the commonly used methods of the particle swarm optimization(PSO)and the Genetic Algorithm (GA),the results demonstrate that FOA-SVMhas the fastest recognition speed and the highest recognition rate.

  18. Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2015-01-01

    Full Text Available Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs by using local characteristic-scale decomposition (LCD method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM classifier based on Chemical Reaction Optimization (CRO algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

  19. Classifying Human Body Acceleration Patterns Using a Hierarchical Temporal Memory

    Science.gov (United States)

    Sassi, Federico; Ascari, Luca; Cagnoni, Stefano

    This paper introduces a novel approach to the detection of human body movements during daily life. With the sole use of one wearable wireless triaxial accelerometer attached to one's chest, this approach aims at classifying raw acceleration data robustly, to detect many common human behaviors without requiring any specific a-priori knowledge about movements. The proposed approach consists of feeding sensory data into a specifically trained Hierarchical Temporal Memory (HTM) to extract invariant spatial-temporal patterns that characterize different body movements. The HTM output is then classified using a Support Vector Machine (SVM) into different categories. The performance of this new HTM+SVM combination is compared with a single SVM using real-word data corresponding to movements like "standing", "walking", "jumping" and "falling", acquired from a group of different people. Experimental results show that the HTM+SVM approach can detect behaviors with very high accuracy and is more robust, with respect to noise, than a classifier based solely on SVMs.

  20. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

    2016-07-01

    The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

  1. SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

    Science.gov (United States)

    Suresh, V; Parthasarathy, S

    2014-01-01

    We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

  2. Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

    Science.gov (United States)

    Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

    2017-09-01

    In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

  3. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    Directory of Open Access Journals (Sweden)

    HungLinh Ao

    2014-01-01

    Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

  4. Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

    Science.gov (United States)

    Hassanpour, Saeed; Langlotz, Curtis P; Amrhein, Timothy J; Befera, Nicholas T; Lungren, Matthew P

    2017-04-01

    The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices. An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system. The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied. The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.

  5. A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar

    Directory of Open Access Journals (Sweden)

    Nicoletta Roberto

    2017-07-01

    Full Text Available An algorithm based on a support vector machine (SVM is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.

  6. Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

    Directory of Open Access Journals (Sweden)

    Laurel Ballanti

    2016-05-01

    Full Text Available The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM and random forest (RF, have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.

  7. Human Behavior Classification Using Multi-Class Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yogameena, B.

    2010-01-01

    Full Text Available Problem statement: In computer vision and robotics, one of the typical tasks is to identify specific objects in an image and to determine each object’s position and orientation relative to coordinate system. This study presented a Multi-class Relevance Vector machine (RVM classification algorithm which classifies different human poses from a single stationary camera for video surveillance applications. Approach: First the foreground blobs and their edges are obtained. Then the relevance vector machine classification scheme classified the normal and abnormal behavior. Results: The performance proposed by our method was compared with Support Vector Machine (SVM and multi-class support vector machine. Experimental results showed the effectiveness of the method. Conclusion: It is evident that RVM has good accuracy and lesser computational than SVM.

  8. Study on flaw identification of ultrasonic signal for large shafts based on optimal support vector machine

    Institute of Scientific and Technical Information of China (English)

    Zhao Xiufen; Yin Guofu; Tian Guiyun; Yin Ying

    2008-01-01

    Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.

  9. 利用SVM改进Adaboost算法的人脸检测精度%IMPROVING FACE DETECTION ACCURACY IN ADABOOST ALGORITHM WITH SVM

    Institute of Scientific and Technical Information of China (English)

    王志伟; 张晓龙; 梁文豪

    2011-01-01

    提出利用SVM分类方法改进Adaboost算法的人脸检测精度.该方法先通过Adaboost算法找出图像中的候选人脸区域,根据训练样本集中的人脸和非人脸样本训练出分类器支持向量机(SVM),然后通过SVM分类器从候选人脸区域中最终确定人脸区域.实验结果证明,SVM分类算法可以提高检测精度,使检测算法具有更好的检测效果.%This paper presents an approach to improve the face detection accuracy in Adaboost algorithm with SVM. Firstly, the method finds out candidate regions of the human face in the image, and trains the classifier of support vector machine (SVM) according to human face samples and non-face samples in the training sample set, then eventually determine the region of human face from candidate face regions by SVM classifier. Experimental results show that the SVM classifying algorithm can improve the detection accuracy and makes the detection algorithm better in detection efficiency.

  10. Signal peptide discrimination and cleavage site identification using SVM and NN.

    Science.gov (United States)

    Kazemian, H B; Yusuf, S A; White, K

    2014-02-01

    About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model.

  11. Subjective Machine Classifiers

    NARCIS (Netherlands)

    Reidsma, Dennis; op den Akker, Hendrikus J.A.; Nijholt, A.; Nijholt, Antinus; Pantic, Maja; Pantic, M.; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

    2008-01-01

    Many interesting phenomena in conversations require interpretative judgements by the annotators. This leads to data which is annotated with lower levels of agreement due to the differences in how annotators interpret conversations. Instead of throwing away this data we show how and when we can

  12. Multi-classification algorithm and its realization based on least square support vector machine algorithm

    Institute of Scientific and Technical Information of China (English)

    Fan Youping; Chen Yunping; Sun Wansheng; Li Yu

    2005-01-01

    As a new type of learning machine developed on the basis of statistics learning theory, support vector machine (SVM) plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifier. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM(LS-SVM) and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-classification is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and two-spirals to measure the performance of the classifier.

  13. Detection of two intermixed invasive woody species using color infrared aerial imagery and the support vector machine classifier

    Science.gov (United States)

    Mirik, Mustafa; Chaudhuri, Sriroop; Surber, Brady; Ale, Srinivasulu; James Ansley, R.

    2013-01-01

    Both the evergreen redberry juniper (Juniperus pinchotii Sudw.) and deciduous honey mesquite (Prosopis glandulosa Torr.) are destructive and aggressive invaders that affect rangelands and grasslands of the southern Great Plains of the United States. However, their current spatial extent and future expansion trends are unknown. This study was aimed at: (1) exploring the utility of aerial imagery for detecting and mapping intermixed redberry juniper and honey mesquite while both are in full foliage using the support vector machine classifier at two sites in north central Texas and, (2) assessing and comparing the mapping accuracies between sites. Accuracy assessments revealed that the overall accuracies were 90% with the associated kappa coefficient of 0.86% and 89% with the associated kappa coefficient of 0.85 for sites 1 and 2, respectively. Z-statistics (0.102<1.96) used to compare the classification results for both sites indicated an insignificant difference between classifications at 95% probability level. In most instances, juniper and mesquite were identified correctly with <7% being mistaken for the other woody species. These results indicated that assessment of the current infestation extent and severity of these two woody species in a spatial context is possible using aerial remote sensing imagery.

  14. A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

    Science.gov (United States)

    Lee, Yu-Hao; Hsieh, Ya-Ju; Shiah, Yung-Jong; Lin, Yu-Huei; Chen, Chiao-Yun; Tyan, Yu-Chang; GengQiu, JiaCheng; Hsu, Chung-Yao; Chen, Sharon Chia-Ju

    2017-04-01

    To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

  15. 可保证分类性能的最小二乘支持向量机%Least squares support vector machine classifiers with guaranteed classification performance

    Institute of Scientific and Technical Information of China (English)

    徐金宝; 廖雷; 业巧林

    2009-01-01

    Support Vector Machine (SVM) is one of focuses of research and application in classification.A new least-squares-based algorithm that introduces a within-class scatter with guaranteed classification performance(VSLSVM) in the design of least squares support vector machines(LS-SVM) is presented.This algorithm can obtain better correctness that reformulates primal LS-SVM problems with optimality criterion Min w'Mw where w is the weight vector corresponding the primal LS-SVM problems,M is the within-class scatter matrix.This method only requires to solve a linear system instead of a quadratic programming problem. Experiments are included to compare SVM and Suykens' approach.%当前支持向量机是分类研究与应用的一个热点.提出了一个新的最小二乘支持向量机算法,该算法向最小二乘支持向量机(LS-SVM)优化模型中融入了类内散度(VSLSVM)思想,即用优化准则Min w′Mw对原LS-SVM进行重组合,w为对应LS-SVM中的权向量,M是类内散度矩阵.提出的方法仅仅需要求解一个线性系统而不是凸规划问题,实验主要对SVM和Suykens等人的方法进行了比较,并验证了提出的算法的有效性.

  16. LMD based features for the automatic seizure detection of EEG signals using SVM.

    Science.gov (United States)

    Zhang, Tao; Chen, Wanzhong

    2016-09-20

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  17. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  18. SVM classification model in depression recognition based on mutation PSO parameter optimization

    Directory of Open Access Journals (Sweden)

    Zhang Ming

    2017-01-01

    Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

  19. SEMI-SUPERVISED RADIO TRANSMITTER CLASSIFICATION BASED ON ELASTIC SPARSITY REGULARIZED SVM

    Institute of Scientific and Technical Information of China (English)

    Hu Guyu; Gong Yong; Chen Yande; Pan Zhisong; Deng Zhantao

    2012-01-01

    Non-collaborative radio transmitter recognition is a significant but challenging issue,sinceit is hard or costly to obtain labeled training data samples.In order to make effective use of the unlabeled samples which can be obtained much easier,a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification.ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term.Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM,which use only labeled samples for classification,and also outperforms semi-supervised classifier LapSVM based on manifold regularization.

  20. A systematic comparison of supervised classifiers.

    Directory of Open Access Journals (Sweden)

    Diego Raphael Amancio

    Full Text Available Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM. In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

  1. Overlaid caption extraction in news video based on SVM

    Science.gov (United States)

    Liu, Manman; Su, Yuting; Ji, Zhong

    2007-11-01

    Overlaid caption in news video often carries condensed semantic information which is key cues for content-based video indexing and retrieval. However, it is still a challenging work to extract caption from video because of its complex background and low resolution. In this paper, we propose an effective overlaid caption extraction approach for news video. We first scan the video key frames using a small window, and then classify the blocks into the text and non-text ones via support vector machine (SVM), with statistical features extracted from the gray level co-occurrence matrices, the LH and HL sub-bands wavelet coefficients and the orientated edge intensity ratios. Finally morphological filtering and projection profile analysis are employed to localize and refine the candidate caption regions. Experiments show its high performance on four 30-minute news video programs.

  2. SVM-T-RFE: a novel gene selection algorithm for identifying metastasis-related genes in colorectal cancer using gene expression profiles.

    Science.gov (United States)

    Li, Xiaobo; Peng, Sihua; Chen, Jian; Lü, Bingjian; Zhang, Honghe; Lai, Maode

    2012-03-09

    Although metastasis is the principal cause of death cause for colorectal cancer (CRC) patients, the molecular mechanisms underlying CRC metastasis are still not fully understood. In an attempt to identify metastasis-related genes in CRC, we obtained gene expression profiles of 55 early stage primary CRCs, 56 late stage primary CRCs, and 34 metastatic CRCs from the expression project in Oncology (http://www.intgen.org/expo/). We developed a novel gene selection algorithm (SVM-T-RFE), which extends support vector machine recursive feature elimination (SVM-RFE) algorithm by incorporating T-statistic. We achieved highest classification accuracy (100%) with smaller gene subsets (10 and 6, respectively), when classifying between early and late stage primary CRCs, as well as between metastatic CRCs and late stage primary CRCs. We also compared the performance of SVM-T-RFE and SVM-RFE gene selection algorithms on another large-scale CRC dataset and the five public microarray datasets. SVM-T-RFE bestowed SVM-RFE algorithm in identifying more differentially expressed genes, and achieving highest prediction accuracy using equal or smaller number of selected genes. A fraction of selected genes have been reported to be associated with CRC development or metastasis.

  3. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Science.gov (United States)

    McDonnell, Mark D; Tissera, Migel D; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

    2015-01-01

    Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  4. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Directory of Open Access Journals (Sweden)

    Mark D McDonnell

    Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  5. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M.; Wallace, David J.; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R.; Hravnak, Marilyn

    2015-01-01

    OBJECTIVE Use machine-learning (ML) algorithms to classify alerts as real or artifacts in online noninvasive vital sign (VS) data streams to reduce alarm fatigue and missed true instability. METHODS Using a 24-bed trauma step-down unit’s non-invasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral oximetry [SpO2]) recorded at 1/20Hz, and noninvasive oscillometric blood pressure [BP] less frequently, we partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were VS deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained ML algorithms. The best model was evaluated on alerts in the test set to enact online alert classification as signals evolve over time. MAIN RESULTS The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve (AUC) performance of 0.79 (95% CI 0.67-0.93) for SpO2 at the instant the VS first crossed threshold and increased to 0.87 (95% CI 0.71-0.95) at 3 minutes into the alerting period. BP AUC started at 0.77 (95%CI 0.64-0.95) and increased to 0.87 (95% CI 0.71-0.98), while RR AUC started at 0.85 (95%CI 0.77-0.95) and increased to 0.97 (95% CI 0.94–1.00). HR alerts were too few for model development. CONCLUSIONS ML models can discern clinically relevant SpO2, BP and RR alerts from artifacts in an online monitoring dataset (AUC>0.87). PMID:26992068

  6. Kernel Projection Algorithm for Large-Scale SVM Problems

    Institute of Scientific and Technical Information of China (English)

    王家琦; 陶卿; 王珏

    2002-01-01

    Support Vector Machine (SVM) has become a very effective method in sta-tistical machine learning and it has proved that training SVM is to solve Nearest Point pairProblem (NPP) between two disjoint closed convex sets. Later Keerthi pointed out that it isdifficult to apply classical excellent geometric algorithms directly to SVM and so designed anew geometric algorithm for SVM. In this article, a new algorithm for geometrically solvingSVM, Kernel Projection Algorithm, is presented based on the theorem on fixed-points of pro-jection mapping. This new algorithm makes it easy to apply classical geometric algorithmsto solving SVM and is more understandable than Keerthi's. Experiments show that the newalgorithm can also handle large-scale SVM problems. Geometric algorithms for SVM, such asKeerthi's algorithm, require that two closed convex sets be disjoint and otherwise the algo-rithms are meaningless. In this article, this requirement will be guaranteed in theory by usingthe theoretic result on universal kernel functions.

  7. Universum Learning for Multiclass SVM

    OpenAIRE

    Dhar, Sauptik; Ramakrishnan, Naveen; Cherkassky, Vladimir; Shah, Mohak

    2016-01-01

    We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose a span bound for MU-SVM that can be used for model selection thereby avoiding resampling. Empirical results demonstrate the effectiveness of MU-SVM and the proposed bound.

  8. Research on Sina Microblogging Marketing Spam Review Detection Based on Support Vector Machine%基于 SVM 的新浪微博营销类水帖识别研究

    Institute of Scientific and Technical Information of China (English)

    叶施仁; 孙宁

    2015-01-01

    Using tremendous robot accounts to follow product twitters,and review the posts about mar-keting contents is a typical spam issue in Sina microblogging.This method could change the existing public opinions about the involved products and form fake hot topics.Based on similar behaviors from a set of ex-isting spam accounts,we attempt to identify these fake posts.Our method will use SVM to classify them according to text,time,clients and multiplicity among them.The test sets consists of several marketing twitters about automotive products using Sina Weibo APIs.The test results show that our method can find those well disguised reviews by spammers.%研究一种发现水帖的分类算法。该方法利用 SimHash 方法将发帖重复当成类似网页去重的问题处理,发帖内容的重复度和其他特征,如发帖的密集型、帐号名称的相似性,所使用的客户端等特征将用于水帖与正常发帖的分类。该文利用新浪微博 API 下载多个汽车营销账号下的交互数据作为实验数据,并使用 SVM 作为分类器。实验结果表明,该方法能够较好地发现那些伪装性非常好的水军所发布的水帖。

  9. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  10. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

    Science.gov (United States)

    Liu, Xiaoming; Mei, Ming; Liu, Jun; Hu, Wei

    2015-12-01

    Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

  11. A new (semantic) reflexive brain-computer interface: in search for a suitable classifier.

    Science.gov (United States)

    Furdea, A; Ruf, C A; Halder, S; De Massari, D; Bogdan, M; Rosenstiel, W; Matuz, T; Birbaumer, N

    2012-01-15

    The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.

  12. Image and Video Quality Assessment Using Neural Network and SVM

    Institute of Scientific and Technical Information of China (English)

    DING Wenrui; TONG Yubing; ZHANG Qishan; YANG Dongkai

    2008-01-01

    An image and video quality assessment method was developed using neural network and support vector machines (SVM) with the peak signal to noise ratio (PSNR) and the structure similarity indexes used to describe image quality. The neural network was used to obtain the mapping functions between the objec-tive quality assessment indexes and subjective quality assessment. The SVM was used to classify the im-ages into different types which were accessed using different mapping functions. Video quality was as-sessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. Simulation results show that the method accurately accesses image quality. The monotonicity of the method for images is 6.94% higher than with the PSNR method, and the root mean square error is at least 35.90% higher than with the PSNR.

  13. Relative Attribute SVM+ Learning for Age Estimation.

    Science.gov (United States)

    Wang, Shengzheng; Tao, Dacheng; Yang, Jie

    2016-03-01

    When estimating age, human experts can provide privileged information that encodes the facial attributes of aging, such as smoothness, face shape, face acne, wrinkles, and bags under-eyes. In automatic age estimation, privileged information is unavailable to test images. To overcome this problem, we hypothesize that asymmetric information can be explored and exploited to improve the generalizability of the trained model. Using the learning using privileged information (LUPI) framework, we tested this hypothesis by carefully defining relative attributes for support vector machine (SVM+) to improve the performance of age estimation. We term this specific setting as relative attribute SVM+ (raSVM+), in which the privileged information enables separation of outliers from inliers at the training stage and effectively manipulates slack variables and age determination errors during model training, and thus guides the trained predictor toward a generalizable solution. Experimentally, the superiority of raSVM+ was confirmed by comparing it with state-of-the-art algorithms on the face and gesture recognition research network (FG-NET) and craniofacial longitudinal morphological face aging databases. raSVM+ is a promising development that improves age estimation, with the mean absolute error reaching 4.07 on FG-NET.

  14. IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST SENSITIVE CRITERION FOR C-SVM

    Directory of Open Access Journals (Sweden)

    M’hamed Bilal Abidine

    2013-11-01

    Full Text Available The growing population of elders in the society calls for a new approach in care giving. By inferring what activities elderly are performing in their houses it is possible to determine their physical and cognitive capabilities. In this paper we show the potential of important discriminative classifiers namely the Soft-Support Vector Machines (C-SVM, Conditional Random Fields (CRF and k-Nearest Neighbors (k-NN for recognizing activities from sensor patterns in a smart home environment. We address also the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classifiers. Cost sensitive learning is attractive under most imbalanced circumstances, but it is difficult to determine the precise misclassification costs in practice. We introduce a new criterion for selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed criterion outperforms the state-of-the-art discriminative methods in activity recognition.

  15. Detection of Cross Site Scripting Attack in Wireless Networks Using n-Gram and SVM

    Directory of Open Access Journals (Sweden)

    Jun-Ho Choi

    2012-01-01

    Full Text Available Large parts of attacks targeting the web are aiming at the weak point of web application. Even though SQL injection, which is the form of XSS (Cross Site Scripting attacks, is not a threat to the system to operate the web site, it is very critical to the places that deal with the important information because sensitive information can be obtained and falsified. In this paper, the method to detect themalicious SQL injection script code which is the typical XSS attack using n-Gram indexing and SVM (Support Vector Machine is proposed. In order to test the proposed method, the test was conducted after classifying each data set as normal code and malicious code, and the malicious script code was detected by applying index term generated by n-Gram and data set generated by code dictionary to SVM classifier. As a result, when the malicious script code detection was conducted using n-Gram index term and SVM, the superior performance could be identified in detecting malicious script and the more improved results than existing methods could be seen in the malicious script code detection recall.

  16. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

    Directory of Open Access Journals (Sweden)

    Yu Yang

    2008-05-01

    Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

  17. 基于加权聚类质心的 SVM 不平衡分类方法%Support vector machine imbalanced data classification based on weighted clustering centroid

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

    Classification of imbalanced data has become a research hot topic in machine learning .Traditional classi-fication algorithms assume that different classes have balanced distribution or equal misclassification cost , thus, making it hard to get ideal result of classifications .A support vector machine (SVM) classification method based on weighted clustering centroid was proposed in this paper .First, unsupervised clustering was applied to the positive and negative samples respectively to extract the clustering centroid of each clustering , which was represented the most in compactness of the clustering sample .Next, all clustering centroids formed a new set of balance training .In order to minimize the information loss during clustering , each clustering centroid was associated with a weight factor that was defined proportional to the number of samples of the class .Finally, all clustering centroids and weight fac-tors participated in the training of the improved SVM model .Experimental results show that the proposed method can make the sample selected from model train sets more typical and improve the classification performance better than other sampling techniques for dealing with imbalanced data .%  不平衡数据分类是机器学习研究的热点问题,传统分类算法假定不同类别具有平衡分布或误分代价相同,难以得到理想的分类结果。提出一种基于加权聚类质心的SVM分类方法,在正负类样本上分别进行聚类,对每个聚类,用聚类质心和权重因子代表聚类内样本分布和数量,相等类别数量的质心和权重因子参与SVM模型训练。实验结果表明,该方法使模型的训练样本具有较高的代表性,分类性能与其他采样方法相比得到了提升。

  18. An S-Transform and Support Vector Machine (SVM-Based Online Method for Diagnosing Broken Strands in Transmission Lines

    Directory of Open Access Journals (Sweden)

    Caxin Sun

    2011-08-01

    Full Text Available During their long-term outdoor field service, overhead transmission lines will be exposed to strikes by lightning, corrosion by chemical contaminants, ice-shedding, wind vibration of conductors, line galloping, external destructive forces and so on, which will generally cause a series of latent faults such as aluminum strand fracture. This may lead to broken transmission lines which will have a very strong impact on the safe operation of power grids that if the latent faults cannot be recognized and fixed as soon as possible. The detection of broken strands in transmission lines using inspection robots equipped with suitable detectors is a method with good prospects. In this paper, a method for detecting broken strands in transmission lines using an eddy current transducer (ECT carried by a robot is developed, and an approach for identifying broken strands in transmission lines based on an S-transform is proposed. The proposed approach utilizes the S-transform to extract the module and phase information at each frequency point from detection signals. Through module phase and comparison, the characteristic frequency points are ascertained, and the fault information of the detection signal is constructed. The degree of confidence of broken strand identification is defined by the Shannon fuzzy entropy (SFE-BSICD. The proposed approach combines module information while utilizing phase information, SFE-BSICD, and the energy, so the reliability is greatly improved. These characteristic qualities of broken strands in transmission lines are used as the input of a multi-classification SVM, allowing the number of broken strands to be determined. Through experimental field verification, it can be shown that the proposed approach displays high accuracy and the SFE-BSICD is defined reasonably.

  19. PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    Ye Feng; Song Yonglun; Li Di; Lai Yizong

    2003-01-01

    A quality monitoring method by means of support vector machines (SVM) for robotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the welding process signal,a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. Under the samples obtained from auto parts welding production line, the learning machine with a radial basis function kernel shows good performance. And this method can be feasible to identify defect online in welding production.

  20. Support vector machine based estimation of remaining useful life: current research status and future trends

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Hong Zhong; Wang, Hai Kun; Li, Yan Feng; Zhang, Longlong; Liu, Zhiliang [University of Electronic Science and Technology of China, Chengdu (China)

    2015-01-15

    Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data. Many SVM based methods have been proposed to predict RUL of some key components. We did a literature review related to SVM based RUL estimation within a decade. The references reviewed are classified into two categories: improved SVM algorithms and their applications to RUL estimation. The latter category can be further divided into two types: one, to predict the condition state in the future and then build a relationship between state and RUL; two, to establish a direct relationship between current state and RUL. However, SVM is seldom used to track the degradation process and build an accurate relationship between the current health condition state and RUL. Based on the above review and summary, this paper points out that the ability to continually improve SVM, and obtain a novel idea for RUL prediction using SVM will be future works.

  1. Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

    Energy Technology Data Exchange (ETDEWEB)

    Gautam, R.S.; Singh, D.; Mittal, A.; Sajin, P. [Indian Institute for Technology, Roorkee (India)

    2008-07-01

    The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots.

  2. MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM.Combined the LS-SVM with the Multi-Resolution Analysis (MRA), this letter proposes the Multi-resolution LS-SVM (MLS-SVM). The proposed algorithm has the same theoretical framework as MRA but with better approximation ability. At a fixed scale MLS-SVM is a classical LS-SVM, but MLS-SVM can gradually approximate the target function at different scales. In experiments, the MLS-SVM is used for nonlinear system identification, and achieves better identification accuracy.

  3. A three-stage expert system based on support vector machines for thyroid disease diagnosis.

    Science.gov (United States)

    Chen, Hui-Ling; Yang, Bo; Wang, Gang; Liu, Jie; Chen, Yi-Dong; Liu, Da-You

    2012-06-01

    In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

  4. Loop-length-dependent SVM prediction of domain linkers for high-throughput structural proteomics.

    Science.gov (United States)

    Ebina, Teppei; Toh, Hiroyuki; Kuroda, Yutaka

    2009-01-01

    The prediction of structural domains in novel protein sequences is becoming of practical importance. One important area of application is the development of computer-aided techniques for identifying, at a low cost, novel protein domain targets for large-scale functional and structural proteomics. Here, we report a loop-length-dependent support vector machine (SVM) prediction of domain linkers, which are loops separating two structural domains. (DLP-SVM is freely available at: http://www.tuat.ac.jp/ approximately domserv/cgi-bin/DLP-SVM.cgi.) We constructed three loop-length-dependent SVM predictors of domain linkers (SVM-All, SVM-Long and SVM-Short), and also built SVM-Joint, which combines the results of SVM-Short and SVM-Long into a single consolidated prediction. The performances of SVM-Joint were, in most aspects, the highest, with a sensitivity of 59.7% and a specificity of 43.6%, which indicated that the specificity and the sensitivity were improved by over 2 and 3% respectively, when loop-length-dependent characteristics were taken into account. Furthermore, the sensitivity and specificity of SVM-Joint were, respectively, 37.6 and 17.4% higher than those of a random guess, and also superior to those of previously reported domain linker predictors. These results indicate that SVMs can be used to predict domain linkers, and that loop-length-dependent characteristics are useful for improving SVM prediction performances.

  5. Development and evaluation of cost-sensitive universum-SVM.

    Science.gov (United States)

    Dhar, Sauptik; Cherkassky, Vladimir

    2015-04-01

    Many machine learning applications involve analysis of high-dimensional data, where the number of input features is larger than/comparable to the number of data samples. Standard classification methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. One such new learning methodology is called learning through contradiction or Universum-support vector machine (U-SVM). Recent studies have shown U-SVM to be quite effective for sparse high-dimensional data sets. However, all these earlier studies have used balanced data sets with equal misclassification costs. This paper extends the U-SVM formulation to problems with different misclassification costs, and presents practical conditions for the effectiveness of this cost-sensitive U-SVM. Several empirical comparisons are presented to validate the proposed approach.

  6. Fatigue Life Prediction of Ductile Iron Based on DE-SVM Algorithm

    Science.gov (United States)

    Yiqun, Ma; Xiaoping, Wang; lun, An

    the model, predicting fatigue life of ductile iron, based on SVM (Support Vector Machine, SVM) has been established. For it is easy to fall into local optimum during parameter optimization of SVM, DE (Differential Evolution algorithm, DE) algorithm was adopted to optimize to improve prediction precision. Fatigue life of ductile iron is predicted combining with concrete examples, and simulation experiment to optimize SVM is conducted adopting GA (Genetic Algorithm), ACO (Ant Colony Optimization) and POS (Partial Swarm Optimization). Results reveal that DE-SVM algorithm is of a better prediction performance.

  7. Online LS-SVM for function estimation and classification

    Institute of Scientific and Technical Information of China (English)

    Jianghua Liu; Jia-pin Chen; Shan Jiang; Junshi Cheng

    2003-01-01

    An online algorithm for training LS-SVM (Least Square Support Vector Machines) was proposed for the application of function estimation and classification. Online LS-SVM means that LS-SVM can be trained in an incremental way, and can be pruned to get sparse approximation in a decremental way. When a SV (Support Vector) is added or removed, the online algorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Online algorithm is especially useful to realistic function estimation problem such as system identification. The experiments with benchmark function estimation problem and classification problem show the validity of this online algorithm.

  8. Predication of Crane Condition Parameters Based on SVM and AR

    Science.gov (United States)

    Xiuzhong, Xu; Xiong, Hu; Congxiao, Zhou

    2011-07-01

    Through statistic analysis of vibration signals of motor on the container crane hoisting mechanism in a port, the feature vectors with vibration are obtained. Through data preprocessing and training data, Training models of condition parameters based on support vector machine (SVM) are established. The testing data of condition monitoring parameters can be predicted by the training models. During training the models, the penalty parameter and kernel function of model are optimized by cross validation. In order to analysis the accurate of SVM model, autoregressive model is used to predict the trend of vibration. The research showed the predicted results of model using SVM are better than the results by autoregressive (AR) modeling.

  9. Hybrid SVM/HMM Method for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    刘江华; 陈佳品; 程君实

    2004-01-01

    A face recognition system based on Support Vector Machine (SVM) and Hidden Markov Model (HMM) has been proposed. The powerful discriminative ability of SVM is combined with the temporal modeling ability of HMM. The output of SVM is moderated to be probability output, which replaces the Mixture of Gauss (MOG) in HMM. Wavelet transformation is used to extract observation vector, which reduces the data dimension and improves the robustness.The hybrid system is compared with pure HMM face recognition method based on ORL face database and Yale face database. Experiments results show that the hybrid method has better performance.

  10. Classification of 5-HT1A receptor agonists and antagonists using GA-SVM method

    Institute of Scientific and Technical Information of China (English)

    Xue-lian ZHU; Hai-yan CAI; Zhi-jian XU; Yong WANG; He-yao WANG; Ao ZHANG; Wei-liang ZHU

    2011-01-01

    Aim:To construct a reliable computational model for the classification of agonists and antagonists of 5-HT1A receptor.Methods:Support vector machine (SVM),a well-known machine learning method,was employed to build a prediction model,and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters,C and r of the SVM model.The overall dataset used in this study comprised 284 ligands of the 5-HT1A receptor with diverse structures reported in the literatures.Results:A SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT1A receptor.The predictive accuracy for training and test sets was 0.942 and 0.865,respectively.For compounds with probability estimate higher than 0.7,the predictive accuracy of the model for training and test sets was 0.954 and 0.927,respectively.To further validate our model,the receiver operating characteristic (ROC) curve was plotted,and the Area-Under-the-ROC-Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set.Conclusion:A reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT1A receptor.To our knowledge,this is the first effort for the classification of 5-HT1A receptor agonists and antagonists based on a diverse dataset.This method may be used to classify the ligands of other members of the GPCR family.

  11. Combination of designed immune based classifiers for ERP assessment in a P300-based GKT

    Directory of Open Access Journals (Sweden)

    Mohammad Hassan Moradi

    2012-08-01

    Full Text Available Constructing a precise classifier is an important issue in pattern recognition task. Combination the decision of several competing classifiers to achieve improved classification accuracy has become interested in many research areas. In this study, Artificial Immune system (AIS as an effective artificial intelligence technique was used for designing of several efficient classifiers. Combination of multiple immune based classifiers was tested on ERP assessment in a P300-based GKT (Guilty Knowledge Test. Experiment results showed that the proposed classifier named Compact Artificial Immune System (CAIS was a successful classification method and could be competitive to other classifiers such as K-nearest neighbourhood (KNN, Linear Discriminant Analysis (LDA and Support Vector Machine (SVM. Also, in the experiments, it was observed that using the decision fusion techniques for multiple classifier combination lead to better recognition results. The best rate of recognition by CAIS was 80.90% that has been improved in compare to other applied classification methods in our study.

  12. Estimation of Remaining Useful Life of Bearings Based on Nested Dichotomy Classifier – A Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    R. Satishkumar

    2016-02-01

    Full Text Available Rolling element bearings play a vital role for maintaining the reliability metrics in all rotating machineries. The downtime due to these bearing failures are now in increasing trend. In general manufacturing environment most of the time the bearings are replaced only after an indication or symptom due to the complexities of deployments for condition monitoring techniques. This paper emphasis on estimating the remaining useful life of bearing using Nested dichotomy classifier. Vibration signals were acquired for a bearing from day one of its operation till it fails naturally through a piezoelectric accelerometer and the features are extracted using the defined statistical features. The best contributing features are selected and classified using the Nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. The effectiveness of these classifiers was analyzed and compared.

  13. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.

    Science.gov (United States)

    Gaonkar, Bilwaj; T Shinohara, Russell; Davatzikos, Christos

    2015-08-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification.

  14. Support vector machine for classification of walking conditions using miniature kinematic sensors.

    Science.gov (United States)

    Lau, Hong-Yin; Tong, Kai-Yu; Zhu, Hailong

    2008-06-01

    A portable gait analysis and activity-monitoring system for the evaluation of activities of daily life could facilitate clinical and research studies. This current study developed a small sensor unit comprising an accelerometer and a gyroscope in order to detect shank and foot segment motion and orientation during different walking conditions. The kinematic data obtained in the pre-swing phase were used to classify five walking conditions: stair ascent, stair descent, level ground, upslope and downslope. The kinematic data consisted of anterior-posterior acceleration and angular velocity measured from the shank and foot segments. A machine learning technique known as support vector machine (SVM) was applied to classify the walking conditions. SVM was also compared with other machine learning methods such as artificial neural network (ANN), radial basis function network (RBF) and Bayesian belief network (BBN). The SVM technique was shown to have a higher performance in classification than the other three methods. The results using SVM showed that stair ascent and stair descent could be distinguished from each other and from the other walking conditions with 100% accuracy by using a single sensor unit attached to the shank segment. For classification results in the five walking conditions, performance improved from 78% using the kinematic signals from the shank sensor unit to 84% by adding signals from the foot sensor unit. The SVM technique with the portable kinematic sensor unit could automatically recognize the walking condition for quantitative analysis of the activity pattern.

  15. [SVM-based spectral recognition of corn and weeds at seedling stage in fields].

    Science.gov (United States)

    Deng, Wei; Zhang, Lu-Da; He, Xiong-Kui; Mueller, J; Zeng, Ai-Jun; Song, Jian-Li; Liu, Ya-Jia; Zhou, Ji-Zhong; Chen, Ji; Wang, Xu

    2009-07-01

    A handheld FieldSpec 3 Spectroradiometer manufactured by ASD Incorporated Company in USA was used to measure the spectroscopic data of canopies of seedling corns, Dchinochloa crasgalli, and Echinochloa crusgalli weeds within the 350-2 500 nm wavelength range in the field. Each canopy was measured five times continuously. The five original spectroscopic data were averaged over the whole wavelength range in order to eliminate random noise. Then the averaged original data were converted into reflectance data, and the unsmooth parts of reflectance spectral curves with large noise were removed. The effective wavelength range for spectral data process was selected as 350-1 300 and 1 400-1 800 nm. Support vector machine (SVM) was chosen as a method of pattern recognition in this paper. SVM has the advantages of solving the problem of small sample size, being able to reach a global optimization, minimization of structure risk, and having higher generalization capability. Two classes of classifier SVM models were built up respectively using "linear", "polynomial", "RBF"(radial basis function), and "mlp (multilayer perception)" kernels. Comparison of different kernel functions for SVM shows that higher precision can be obtained by using "polynomial" kernel function with 3 orders. The accuracy can be above 80%, but the SV ratio is relatively low. On the basis of two-class classification model, taking use of voting procedure, a model based on one-against-one-algorithm multi-class classification SVM was set up. The accuracy reaches 80%. Although the recognition accuracy of the model based on SVM algorithm is not above 90%, the authors still think that the research on weeds recognition using spectrum technology combining SVM method discussed in this paper is tremendously significant. Because the data used in this study were measured over plant canopies outdoor in the field, the measurement is affected by illumination intensity, soil background, atmosphere temperature and

  16. AREA DETERMINATION OF DIABETIC FOOT ULCER IMAGES USING A CASCADED TWO-STAGE SVM BASED CLASSIFICATION.

    Science.gov (United States)

    Wang, Lei; Pedersen, Peder; Agu, Emmanuel; Strong, Diane; Tulu, Bengisu

    2016-11-23

    It is standard practice for clinicians and nurses to primarily assess patients' wounds via visual examination. This subjective method can be inaccurate in wound assessment and also represents a significant clinical workload. Hence, computer-based systems, especially implemented on mobile devices, can provide automatic, quantitative wound assessment and can thus be valuable for accurately monitoring wound healing status. Out of all wound assessment parameters, the measurement of the wound area is the most suitable for automated analysis. Most of the current wound boundary determination methods only process the image of the wound area along with a small amount of surrounding healthy skin. In this paper, we present a novel approach that uses Support Vector Machine (SVM) to determine the wound boundary on a foot ulcer image captured with an image capture box, which provides controlled lighting, angle and range conditions. The Simple Linear Iterative Clustering (SLIC) method is applied for effective super-pixel segmentation. A cascaded two-stage classifier is trained as follows: in the first stage a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and a set of incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from super-pixels that are used as input for each stage in the classifier training. Specifically, we apply the color and Bag-of-Word (BoW) representation of local Dense SIFT features (DSIFT) as the descriptor for ruling out irrelevant regions (first stage), and apply color and wavelet based features as descriptors for distinguishing healthy tissue from wound regions (second stage). Finally, the detected wound boundary is refined by applying a Conditional Random Field (CRF) image processing technique. We have implemented the wound classification on a Nexus

  17. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  18. An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis

    Directory of Open Access Journals (Sweden)

    Sandeep Chaurasia

    2013-09-01

    Full Text Available Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of machine learning. In this paper we have used an approach by using support vector machine classifier to construct a model that is useful for the breast cancer survivability prediction. We have used both 5 cross and 10 cross validation of variable selection on input feature vectors and the performance measurement through bio-learning class performance while measuring AUC, specificity and sensitivity. The performance of the SVM is much better than the other machine learning classifier.

  19. A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

    Science.gov (United States)

    Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

    2017-08-01

    The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

  20. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  1. Computational identification of human long intergenic non-coding RNAs using a GA-SVM algorithm.

    Science.gov (United States)

    Wang, Yanqiu; Li, Yang; Wang, Qi; Lv, Yingli; Wang, Shiyuan; Chen, Xi; Yu, Xuexin; Jiang, Wei; Li, Xia

    2014-01-01

    Long intergenic non-coding RNAs (lincRNAs) are a new type of non-coding RNAs and are closely related with the occurrence and development of diseases. In previous studies, most lincRNAs have been identified through next-generation sequencing. Because lincRNAs exhibit tissue-specific expression, the reproducibility of lincRNA discovery in different studies is very poor. In this study, not including lincRNA expression, we used the sequence, structural and protein-coding potential features as potential features to construct a classifier that can be used to distinguish lincRNAs from non-lincRNAs. The GA-SVM algorithm was performed to extract the optimized feature subset. Compared with several feature subsets, the five-fold cross validation results showed that this optimized feature subset exhibited the best performance for the identification of human lincRNAs. Moreover, the LincRNA Classifier based on Selected Features (linc-SF) was constructed by support vector machine (SVM) based on the optimized feature subset. The performance of this classifier was further evaluated by predicting lincRNAs from two independent lincRNA sets. Because the recognition rates for the two lincRNA sets were 100% and 99.8%, the linc-SF was found to be effective for the prediction of human lincRNAs.

  2. Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM

    Directory of Open Access Journals (Sweden)

    Shih-Ting Yang

    2013-01-01

    Full Text Available In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM and particle swarm optimization (PSO for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM, were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

  3. Discrimination between Alzheimer's disease and mild cognitive impairment using SOM and PSO-SVM.

    Science.gov (United States)

    Yang, Shih-Ting; Lee, Jiann-Der; Chang, Tzyh-Chyang; Huang, Chung-Hsien; Wang, Jiun-Jie; Hsu, Wen-Chuin; Chan, Hsiao-Lung; Wai, Yau-Yau; Li, Kuan-Yi

    2013-01-01

    In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape) from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA) to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM) and particle swarm optimization (PSO) for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM), were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

  4. Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

    Science.gov (United States)

    Wang, Danshi; Zhang, Min; Cai, Zhongle; Cui, Yue; Li, Ze; Han, Huanhuan; Fu, Meixia; Luo, Bin

    2016-06-01

    An effective machine learning algorithm, the support vector machine (SVM), is presented in the context of a coherent optical transmission system. As a classifier, the SVM can create nonlinear decision boundaries to mitigate the distortions caused by nonlinear phase noise (NLPN). Without any prior information or heuristic assumptions, the SVM can learn and capture the link properties from only a few training data. Compared with the maximum likelihood estimation (MLE) algorithm, a lower bit-error rate (BER) is achieved by the SVM for a given launch power; moreover, the launch power dynamic range (LPDR) is increased by 3.3 dBm for 8 phase-shift keying (8 PSK), 1.2 dBm for QPSK, and 0.3 dBm for BPSK. The maximum transmission distance corresponding to a BER of 1 ×10-3 is increased by 480 km for the case of 8 PSK. The larger launch power range and longer transmission distance improve the tolerance to amplitude and phase noise, which demonstrates the feasibility of the SVM in digital signal processing for M-PSK formats. Meanwhile, in order to apply the SVM method to 16 quadratic amplitude modulation (16 QAM) detection, we propose a parameter optimization scheme. By utilizing a cross-validation and grid-search techniques, the optimal parameters of SVM can be selected, thus leading to the LPDR improvement by 2.8 dBm. Additionally, we demonstrate that the SVM is also effective in combating the laser phase noise combined with the inphase and quadrature (I/Q) modulator imperfections, but the improvement is insignificant for the linear noise and separate I/Q imbalance. The computational complexity of SVM is also discussed. The relatively low complexity makes it possible for SVM to implement the real-time processing.

  5. Evolutionary Support Vector Machines for Transient Stability Monitoring

    Science.gov (United States)

    Dora Arul Selvi, B.; Kamaraj, N.

    2012-03-01

    Currently, power systems are in the need of fast and reliable contingency monitoring systems for the purpose of maintaining stability in the presence of deregulated and open market environment. In this paper, a quick and unfailing transient stability monitoring algorithm that considers both the symmetrical and unsymmetrical faults is presented. support vector machines (SVMs) are employed as pattern classifiers so as to construct fast relation mappings between the transient stability results and the selected input attributes using mutual information. The type of fault is recognized by a SVM classifier and the critical clearing time of the fault is estimated by a support vector regression machine. The SVM parameters are tuned by an elitist multi-objective non-dominated sorting genetic algorithm in such a manner that the best classification and regression performance are accomplished. To demonstrate the good potential of the scheme, IEEE 3 generator system and a South Indian Grid are utilized.

  6. Automated detection of pulmonary nodules in CT: false positive reduction by combining multiple classifiers

    Science.gov (United States)

    Suárez-Cuenca, Jorge Juan; Guo, Wei; Li, Qiang

    2011-03-01

    The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a CAD system for pulmonary nodule detection in CT. We employed CT cases in the publicly available lung image database consortium (LIDC) dataset, which included 85 CT cases with 110 nodules. We first used six individual classifiers for nodule detection in CT, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), and three types of support vector machines (SVM). Five informationfusion methods were then employed to combine the classifiers' outputs for improving detection performance. The five combination methods included two supervised (likelihood ratio method and neural network) and three unsupervised ones (the mean, the product, and the majority-vote of the output scores from the six individual classifiers). Leave-one-caseout was employed to train and test individual classifiers and supervised combination methods. At a sensitivity of 80 %, the numbers of false positives per case for the six individual classifiers were 6.1 for LDA, 19.9 for QDA, 8.6 for ANN, 23.7 for SVM-dot, 17.0 for SVM-poly, and 23.35 for SVM-ANOVA; the numbers of false positives per case for the five combination methods were 3.4 for the majority-vote rule, 6.2 for the mean, 5.7 for the product, 9.7 for the neural network, and 28.1 for the likelihood ratio method. The majority-vote rule achieved higher performance levels than other combination methods. It also achieved higher performance than the best individual classifier, which is not the case for other combination methods.

  7. Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.

    Science.gov (United States)

    Fernandez-Lozano, C; Canto, C; Gestal, M; Andrade-Garda, J M; Rabuñal, J R; Dorado, J; Pazos, A

    2013-01-01

    Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

  8. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

    Science.gov (United States)

    Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

    2017-03-01

    In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

  9. A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology

    Institute of Scientific and Technical Information of China (English)

    Qun Wang; Zhuyun Liu; Zhongren Peng

    2015-01-01

    The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short⁃term travel time forecasting on urban arterials, a prediction model ( PSO⁃SVM) combining support vector machine ( SVM) and particle swarm optimization ( PSO) is developed. Travel time data collected with Bluetooth devices are used to calibrate the proposed model. Field experiments show that the PSO⁃SVM model ’ s error indicators are lower than the single SVM model and the BP neural network (BPNN)model. Particularly, the mean⁃absolute percentage error (MAPE) of PSO⁃SVM is only 9�453 4 %which is less than that of the single SVM model ( 12�230 2 %) and the BPNN model ( 15�314 7 %) . The results indicate that the proposed PSO⁃SVM model is feasible and more effective than other models for short⁃term travel time prediction on urban arterials.

  10. 基于改进萤火虫算法的 SVM 核参数选取%SVM KERNEL PARAMETER SELECTION BASED ON IMPROVED GSO

    Institute of Scientific and Technical Information of China (English)

    杨海; 丁毅; 沈海斌

    2015-01-01

    支持向量机(SVM)是一种性能优异的机器学习算法,其核函数参数的选取对于建模精度以及泛化能力有着重要的影响。提出一种基于改进萤火虫算法的 SVM核函数参数选取方法,通过改进萤火虫位置更新公式并在移动过程中引入亮度特征从而确定最佳的 SVM核函数参数。实验表明,该算法选取的 SVM核函数参数在保证分类器收敛性能的同时,提高了分类精度,取得了良好的优化效果。%Support vector machine (SVM)is a machine learning algorithm with superior performance,the selection of its kernel function parameters greatly affects the modelling accuracy and generalisation ability.This paper proposes an SVMkernel function parameter selection method,it is based on the improved glowworm swarm optimisation (GSO).By improving the glowworm position update formula and introducing brightness feature to the process of moving,the method determines the optimal parameters of SVM kernel function.Experiment shows that the SVM kernel function parameters selected by the method improves classification accuracy while guaranteeing the convergence performance of the classifier,and thus achieves good optimisation effect.

  11. Compression method based on training dataset of SVM

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The method to compress the training dataset of Support Vector Machine (SVM) based on the character of the Support Vector Machine is proposed.First,the distance between the unit in two training datasets,and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset.The time spent in training SVM with the training dataset compressed by the method is shortened obviously.The result of the experiment shows that the algorithm is effective.

  12. CCH-based geometric algorithms for SVM and applications

    Institute of Scientific and Technical Information of China (English)

    Xin-jun PENG; Yi-fei WANG

    2009-01-01

    The support vector machine (SVM) is a novel machine learning tool in data mining. In this paper, the geometric approach based on the compressed convex hull (CCH) with a mathematical framework is introduced to solve SVM classification problems. Compared with the reduced convex hull (RCH), CCH preserves the shape of geometric solids for data sets; meanwhile, it is easy to give the necessary and sufficient condition for determining its extreme points. As practical applications of CCH, spare and probabilistic speed-up geometric algorithms are developed. Results of numerical experiments show that the proposed algorithms can reduce kernel calculations and display nice performances.

  13. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  14. Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace%Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

    Institute of Scientific and Technical Information of China (English)

    LIU Li-mei; WANG An-na; SHA Mo; ZHAO Feng-yun

    2011-01-01

    Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine (LS-SVM) is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.

  15. A pixel-based color image segmentation using support vector machine and fuzzy C-means.

    Science.gov (United States)

    Wang, Xiang-Yang; Zhang, Xian-Jin; Yang, Hong-Ying; Bu, Juan

    2012-09-01

    Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

  16. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  17. FEATURE RANKING BASED NESTED SUPPORT VECTOR MACHINE ENSEMBLE FOR MEDICAL IMAGE CLASSIFICATION.

    Science.gov (United States)

    Varol, Erdem; Gaonkar, Bilwaj; Erus, Guray; Schultz, Robert; Davatzikos, Christos

    2012-01-01

    This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer's disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.

  18. Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

    Indian Academy of Sciences (India)

    Liu Zhong-bao; Song Wen-ai; Zhang Jing; Zhao Wen-juan

    2017-06-01

    Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher’s Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

  19. Feature Selection Based on the SVM Weight Vector for Classification of Dementia.

    Science.gov (United States)

    Bron, Esther E; Smits, Marion; Niessen, Wiro J; Klein, Stefan

    2015-09-01

    Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previously showed clusters of relevant voxels in dementia-related brain regions, they have not yet been used for feature selection. Therefore, we introduce two novel feature selection methods based on p-maps using a direct approach (filter) and an iterative approach (wrapper). To evaluate these p-map feature selection methods, we compared them with methods based on the SVM weight vector directly, t-statistics, and expert knowledge. We used MRI data from the Alzheimer's disease neuroimaging initiative classifying Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients who converted to AD (MCIc), MCI patients who did not convert to AD (MCInc), and cognitively normal controls (CN). Features for each voxel were derived from gray matter morphometry. Feature selection based on the SVM weights gave better results than t-statistics and expert knowledge. The p-map methods performed slightly better than those using the weight vector. The wrapper method scored better than the filter method. Recursive feature elimination based on the p-map improved most for AD-CN: the area under the receiver-operating-characteristic curve (AUC) significantly increased from 90.3% without feature selection to 92.0% when selecting 1.5%-3% of the features. This feature selection method also improved the other classifications: AD-MCI 0.1% improvement in AUC (not significant), MCI-CN 0.7%, and MCIc-MCInc 0.1% (not significant). Although the performance improvement due to feature selection was limited, the methods based on the p-map generally had the best performance, and were therefore better in estimating the relevance of individual features.

  20. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from (18)F-FDG PET/CT images.

    Science.gov (United States)

    Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

    2017-12-01

    This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from (18)F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images

  1. Semi-supervised SVM for individual tree crown species classification

    Science.gov (United States)

    Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

    2015-12-01

    In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

  2. Building multiclass classifiers for remote homology detection and fold recognition

    Directory of Open Access Journals (Sweden)

    Karypis George

    2006-10-01

    Full Text Available Abstract Background Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. Results We present a comprehensive evaluation of a number of methods for building SVM-based multiclass classification schemes in the context of the SCOP protein classification. These methods include schemes that directly build an SVM-based multiclass model, schemes that employ a second-level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. Conclusion Analyzing the performance achieved by the different approaches on four different datasets we show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to not only lead to lower error rates but also reduce the number of errors in which a superfamily is assigned to an entirely different fold and a fold is predicted as being from a different SCOP class. Our results also show that the limited size of the training data makes it hard to learn complex second-level models, and that models of moderate complexity lead to consistently better results.

  3. A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

    Science.gov (United States)

    Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

    2017-03-01

    The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

  4. Using machine learning to classify image features from canine pelvic radiographs: evaluation of partial least squares discriminant analysis and artificial neural network models.

    Science.gov (United States)

    McEvoy, Fintan J; Amigo, José M

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine hip joint on ventrodorsal pelvis radiographs. A training set of images (120 of the hip and 80 from other regions) was used to train a linear partial least squares discriminant analysis (PLS-DA) model and a nonlinear artificial neural network (ANN) model to classify hip images. Performance of the models was assessed using a separate test image set (36 containing hips and 20 from other areas). Partial least squares discriminant analysis model achieved a classification error, sensitivity, and specificity of 6.7%, 100%, and 89%, respectively. The corresponding values for the ANN model were 8.9%, 86%, and 100%. Findings indicated that statistical classification of veterinary images is feasible and has the potential for grouping and classifying images or image features, especially when a large number of well-classified images are available for model training. © 2012 Veterinary Radiology & Ultrasound.

  5. Inferring Planet Occurrence Rates With a Q1-Q17 Kepler Planet Candidate Catalog Produced by a Machine Learning Classifier

    Science.gov (United States)

    Catanzarite, Joseph; Jenkins, Jon Michael; McCauliff, Sean D.; Burke, Christopher; Bryson, Steve; Batalha, Natalie; Coughlin, Jeffrey; Rowe, Jason; mullally, fergal; thompson, susan; Seader, Shawn; Twicken, Joseph; Li, Jie; morris, robert; smith, jeffrey; haas, michael; christiansen, jessie; Clarke, Bruce

    2015-08-01

    NASA’s Kepler Space Telescope monitored the photometric variations of over 170,000 stars, at half-hour cadence, over its four-year prime mission. The Kepler pipeline calibrates the pixels of the target apertures for each star, produces light curves with simple aperture photometry, corrects for systematic error, and detects threshold-crossing events (TCEs) that may be due to transiting planets. The pipeline estimates planet parameters for all TCEs and computes diagnostics used by the Threshold Crossing Event Review Team (TCERT) to produce a catalog of objects that are deemed either likely transiting planet candidates or false positives.We created a training set from the Q1-Q12 and Q1-Q16 TCERT catalogs and an ensemble of synthetic transiting planets that were injected at the pixel level into all 17 quarters of data, and used it to train a random forest classifier. The classifier uniformly and consistently applies diagnostics developed by the Transiting Planet Search and Data Validation pipeline components and by TCERT to produce a robust catalog of planet candidates.The characteristics of the planet candidates detected by Kepler (planet radius and period) do not reflect the intrinsic planet population. Detection efficiency is a function of SNR, so the set of detected planet candidates is incomplete. Transit detection preferentially finds close-in planets with nearly edge-on orbits and misses planets whose orbital geometry precludes transits. Reliability of the planet candidates must also be considered, as they may be false positives. Errors in detected planet radius and in assumed star properties can also bias inference of intrinsic planet population characteristics.In this work we infer the intrinsic planet population, starting with the catalog of detected planet candidates produced by our random forest classifier, and accounting for detection biases and reliabilities as well as for radius errors in the detected population.Kepler was selected as the 10th mission

  6. SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Jagadeesh D.Pujari

    2016-06-01

    Full Text Available Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM and artificial neural network (ANN classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

  7. Application of support vector machine and quantum genetic algorithm in infrared target recognition

    Science.gov (United States)

    Wang, Hongliang; Huang, Yangwen; Ding, Haifei

    2010-08-01

    In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.

  8. PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

    Directory of Open Access Journals (Sweden)

    Yukai Yao

    2015-01-01

    Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

  9. DIAGNOSIS OF DIABETIC RETINOPATHY USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Priya

    2013-07-01

    Full Text Available Diabetic retinopathy (DR is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR and proliferative diabetic retinopathy (PDR. In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN, Bayesian Classification and Support vector machine (SVM are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy” and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.

  10. Graph segmentation and support vector machines for bare earth classification from lidar

    Science.gov (United States)

    Shorter, Nicholas S.; Smith, O'Neil; Smith, Philip; Rahmes, Mark

    2014-06-01

    A novel approach using a support vector machine (SVM) is proposed to classify bare earth points in LiDAR point clouds. Using graph based segmentation, the LiDAR point cloud is segmented into a set of topological components. Several features establishing relationships from those components to their neighboring components are formulated. The SVM is then trained on the segment features to establish a model for the classification of bare earth and non bare earth points. Quantitative results are presented for training and testing the proposed SVM classifier on the ISPRS data set. Using the ISPRS data set as a training set, qualitative results are presented by testing the proposed SVM classifier on data downloaded from Open Topography; which covers a variety of different landscapes and building structures in Frazier Park, California. Despite the data being captured from different sensors, and collected from scenes with different terrain types and building structures, the results shown were processed with no parameter changes. Furthermore, a confidence value is returned indicating how well the unforeseen data fits the SVM's trained model for bare earth recognition.

  11. Gene association study with SVM, MLP and cross-validation for the diagnosis of diseases

    Institute of Scientific and Technical Information of China (English)

    Junying Zhang; Shenling Liu; Yue Wang

    2008-01-01

    Gene association study is one of the major challenges of biochip technology both for gene diagnosis where only a gene subset is responsible for some diseases, and for the treatment of the curse of dimensionality which occurs especially in DNA microarray datasets where there are more than thousands of genes and only a few number of experiments (samples). This paper presents a gene selection method by training linear support vector machine (SVM)/nonlinear MLP (multilayer perceptron) classifiers and testing them with cross-validation for finding a gene subset which is optimal/suboptimal for the diagnosis of binary/multiple disease types. Genes are selected with linear SVM classifier for the diagnosis of each binary disease types pair and tested by leave-one-out cross-validation; then, genes in the gene subset initialized by the union of them are deleted one by one by removing the gene which brings the greatest decrease of the generalization power, for samples, on the gene subset after removal, where generalization is measured by training MLPs with leave-one-out and leave-four-out cross-validations. The proposed method was tested with experiments on real DNA microarray MIT data and NCI data. The result shows that it outperforms conventional SNR method in the separability of the data with expression levels on selected genes. For real DNA microarray MIT/NCI data, which is composed of 7129/2308 effective genes with only 72/64 labeled samples belonging to 2/4 disease classes, only 11/6 genes are selected to be diagnostic genes. The selected genes are tested by the classification of samples on these genes with SVM/MLP with leave-one-out/both leave-one-out and leave-four-out cross-validations. The result of no misclassification indicates that the selected genes can be really considered as diagnostic genes for the diagnosis of the corresponding diseases.

  12. SVM-based prediction of caspase substrate cleavage sites

    Directory of Open Access Journals (Sweden)

    Ranganathan Shoba

    2006-12-01

    Full Text Available Abstract Background Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. Results A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i the tetrapeptide cleavage sites, (ii the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. Conclusion This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

  13. Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Bing Peng

    2015-11-01

    Full Text Available Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC and nearest regularized subspace (NRS. In this work, a weighted residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP, Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are weighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS, SRC, support vector machine (SVM with LBP, and SVM with Gabor features, even in small-sample-size (SSS situations.

  14. The generalization ability of online SVM classification based on Markov sampling.

    Science.gov (United States)

    Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

    2015-03-01

    In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

  15. Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions (Conference Presentation)

    Science.gov (United States)

    Nguyen, Thien; Ahn, Sangtae; Jang, Hyojung; Jun, Sung C.; Kim, Jae G.

    2016-03-01

    Driver's condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver's status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher's linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.

  16. A NOVEL SVM FOR GROUND PENETRATING SYNTHETIC APERTURE RADAR LANDMINE DETECTION

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

  17. Recognition of Tobacco Flue-Curing Phases Based on Image Features and GA-SVM Algorithm%基于图像特征和 GA-SVM 的烤烟烘烤阶段识别研究

    Institute of Scientific and Technical Information of China (English)

    吴娟

    2016-01-01

    密集烤房内烤烟烘烤阶段的自动识别是建立智能化烟叶烘烤系统的重要环节.为了有效地识别烤房内烤烟的烘烤阶段,该文提出了一种基于图像特征和 GA-SVM(Genetic Algorithm-Support Vector Machine)相结合的方法.该方法将机器视觉系统提取的烤烟图像特征作为 SVM 的输入参数,通过 GA 全局搜索特性选取出模型的最优特征子集,最后通过多分类 SVM 实现对烘烤阶段的识别,同时验证了选取特征的有效性.仿真结果表明:从9个原始特征中筛选出5个图像特征,总体识别精度从93.7%提高到96.5%,能有效地识别烤烟的烘烤阶段,具有良好的在线应用前景.%The recognition of tobacco flue-curing phases in bulk curing barn is an important part of building intelligent tobacco curing system.In order to recognize the curing phase effectively,a combination method based on image features and GA-SVM algorithm has been proposed.The proposed method uses machine vision system to extract the tobacco images features which are input to SVM.The optimal feature subset has been selected thanks to the global search ability of GA.The curing phase has been recognized by multi-classifier SVM and the effectiveness been verified either.Simulation results demonstrate that 5 image fea-tures are selected from original 9 features,which makes the overall recognition accuracy increased from 93.7% to 96.5%.The proposed method can effectively recognize tobacco curing phases and has good pros-pects for online applications.

  18. Classification of Land Utilization and Covering Based on Support Vector Machine---with case of Laoha River catachment%基于 SVM 的土地利用/覆盖分类--以老哈河流域为例

    Institute of Scientific and Technical Information of China (English)

    李硕

    2015-01-01

    选取老哈河流域为研究区域,以2007年的两景Landsat5的TM影像为数据源,对该地区进行土地利用/覆盖分类。由于该区域土地覆盖类型复杂,影像较难区分且容易造成错分类。该研究中采用支持向量机( Support Vector Machine,SVM)分类法,通过引入径向基核函数进行非线性变换映射至高维空间,提取它们的非线性特征,增强不同类型之间的可分性,减少错分现象,提高遥感图像分类的精度。通过试验,提取出了2007年的老哈河流域的土地利用/覆盖现状图,以校验该方法的可行性。%The Laoha River catchment is selected as the study catchment.Based on the data source of TM image of Landsat 5 in 2007, classification of the land utilization and covering in the catchment is studied.As the land covering of this catchment is complicated in classification, the images are difficult to separate and easy to classify.In this study, classification method of support vector machine (SVM) is applied.By utilization of radial basis function, the non-linear conversion is conducted to the high-dimensional space, abstrac-ting their non-linear characteristics, strengthening the separation between different types, reducing mistaken classification and improving accuracy of the remote-sense image classification.Through tests, the land utilization and covering status images of the Laoha River catch-ment in 2007 are abstracted to verify the feasibility of this method.

  19. Particulate matter characterization by gray level co-occurrence matrix based support vector machines.

    Science.gov (United States)

    Manivannan, K; Aggarwal, P; Devabhaktuni, V; Kumar, A; Nims, D; Bhattacharya, P

    2012-07-15

    An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.

  20. Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T

    Directory of Open Access Journals (Sweden)

    Fusun Citak-Er

    2014-01-01

    Full Text Available Objective. This study aimed at evaluating linear discriminant analysis (LDA and support vector machine (SVM classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI and clinical parameters. Materials and Methods. Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI, diffusion weighted MRI (DW-MRI, and dynamic contrast enhanced MRI (DCE-MRI estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE was used for eliminating features. Principal component analysis (PCA was applied for data uncorrelation. Results. Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively. Conclusion. SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

  1. Using Syntactic-Based Kernels for Classifying Temporal Relations

    Institute of Scientific and Technical Information of China (English)

    Seyed Abolghasem Mirroshandel; Gholamreza Ghassem-Sani; Mahdy Khayyamian

    2011-01-01

    Temporal relation classification is one of contemporary demanding tasks of natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations, between events or between events and time, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting some useful automatically generated syntactic features to improve the accuracy of classification. Accordingly, a number of novel kernel functions are introduced and evaluated. Our evaluations clearly demonstrate that adding syntactic features results in a considerable improvement over the state-of-the-art method of classifying temporal relations.

  2. The fuzzy gene filter: A classifier performance assesment

    CERN Document Server

    Perez, Meir

    2011-01-01

    The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.

  3. Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review

    Science.gov (United States)

    Quitadamo, L. R.; Cavrini, F.; Sbernini, L.; Riillo, F.; Bianchi, L.; Seri, S.; Saggio, G.

    2017-02-01

    Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

  4. 公私合作(PPP)项目范式选择的决策模型分析——基于SVM分类理论%Decision-making model of public-private partnerships projects' paradigm choice——based upon the SVM classified theory

    Institute of Scientific and Technical Information of China (English)

    胡振; 范秀芳; 董清

    2012-01-01

    以投资规模、VFM和收益方式这三个因素为影响因子,应用SVM分类器的基本原理,构建公私合作项目范式选择的决策模型,结果表明,模型的训练准确率和预测准确率均具有较高的水平,所得模型对于政府进行公共项目公私合作的范式选择,具有较强的指导意义和操作性.%For the purpose of constructing the decision-making model of public-private partnerships projects' paradigm choice, three factors are faken into account; investment scale, VFM and yield basis as the impact factor. By applying the basic principle of SVM classifier, the outcome demonstrated that the disciplinary accuracy and predicted accuracy of the model all have higher level and such model also had strong instructive meaning and operability in terms of the decision-making model of public-private partnerships projects' paradigm choice for the government.

  5. Classification of Cancer-related Death Certificates using Machine Learning

    Directory of Open Access Journals (Sweden)

    Luke Butt

    2013-05-01

    Full Text Available BackgroundCancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities.AimsIn this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated.Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes.ResultsDeath certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032 and false negative rate (0.0297 while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers.ConclusionThe selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with

  6. The VIMOS Public Extragalactic Redshift Survey (VIPERS). A Support Vector Machine classification of galaxies, stars and AGNs

    CERN Document Server

    Malek, K; Pollo, A; Fritz, A; Garilli, B; Scodeggio, M; Iovino, A; Granett, B R; Abbas, U; Adami, C; Arnouts, S; Bel, J; Bolzonella, M; Bottini, D; Branchini, E; Cappi, A; Coupon, J; Cucciati, O; Davidzon, I; De Lucia, G; de la Torre, S; Franzetti, P; Fumana, M; Guzzo, L; Ilbert, O; Krywult, J; Brun, V Le; Fevre, O Le; Maccagni, D; Marulli, F; McCracken, H J; Paioro, L; Polletta, M; Schlagenhaufer, H; Tasca, L A M; Tojeiro, R; Vergani, D; Zanichelli, A; Burden, A; Di Porto, C; Marchetti, A; Marinoni, C; Mellier, Y; Moscardini, L; Nichol, R C; Peacock, J A; Percival, W J; Phleps, S; Wolk, M; Zamorani, G

    2013-01-01

    The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and we apply the techniques to the VIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u*, g', r', i') and NIR data (z', Ks), we develop a classifier for identifying stars, AGNs and galaxies improving the purity of the VIPERS sample. Support Vector Machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classes based on a multidimensional parameter space. In this work, we tailored the SVM for classifying stars, AGNs and galaxies, and applied this classification to the VIPERS data. We train the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys. We tested two SVM classifiers and concluded that including NIR data can significantly improve the efficiency of the classifier. The self-check of the best optical + NIR classifier has shown a 97% accuracy in the classification of galaxies, 97 for stars, and 95 for AGNs ...

  7. A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity

    Directory of Open Access Journals (Sweden)

    Thanh-Dao Tran

    2012-04-01

    Full Text Available Benzo[c]phenanthridine (BCP derivatives were identified as topoisomerase I (TOP-I targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors identified from a large set via a random forest algorithm. Overall accuracy of up to 87% and a Matthews coefficient correlation (MCC of 0.71 were obtained after this SVM classifier was validated internally by a test set of 15 compounds. For two external test sets, 89% and 80% BCP compounds, respectively, were correctly predicted. The results indicated that our SVM model could be used as the filter for designing new BCP compounds with higher TOP-I inhibitory activity.

  8. Forecasting Models for Hydropower Unit Stability Using LS-SVM

    Directory of Open Access Journals (Sweden)

    Liangliang Qiao

    2015-01-01

    Full Text Available This paper discusses a least square support vector machine (LS-SVM approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in Y-direction of lower generator bearing (LGB and pressure in draft tube (DT. A heuristic method such as a neural network using Backpropagation (NNBP is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.

  9. Incremental Training for SVM-Based Classification with Keyword Adjusting

    Institute of Scientific and Technical Information of China (English)

    SUN Jin-wen; YANG Jian-wu; LU Bin; XIAO Jian-guo

    2004-01-01

    This paper analyzed the theory of incremental learning of SVM (support vector machine) and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning.According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the difference between incremental learning and batch learning through the keyword adjusting.The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method.

  10. MOBILE GEO-LOCATION ALGORITHM BASED ON LS-SVM

    Institute of Scientific and Technical Information of China (English)

    Sun Guolin; Guo Wei

    2005-01-01

    Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.

  11. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

    Science.gov (United States)

    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-07-25

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer.

  12. A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2012-01-01

    Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

  13. An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

    Directory of Open Access Journals (Sweden)

    Yasmine Rezgui

    2017-01-01

    Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.

  14. TrSVM:一种基于领域相似性的迁移学习算法%TrSVM: A Transfer Learning Algorithm Using Domain Similarity

    Institute of Scientific and Technical Information of China (English)

    洪佳明; 印鉴; 黄云; 刘玉葆; 王甲海

    2011-01-01

    Transfer learning algorithms focus on reusing related domain data to help solving learning tasks in the target domain. In this paper, we study the problem of inductive transfer learning. Most of the existing algorithms in inductive transfer learning might suffer from the problem of sample selection bias when the number of target domain data is too small. To address this problem, we propose to utilize domain similarity in a new approach. Through detailed discussion about the similarity of related domains, we define the concept of weak domain similarity. Using this concept to give additional constraints on the target classifiers, we develop a simple but effective approach toleverage the useful knowledge from the related domain, so that related domain data can be directly used in the training process. In this way, we are able to make the target classifier less sensitive to the small amount of target training data. Furthermore, we show that a modified SMO method can be applied to optimize the objective function in the algorithm effectively. The new algorithm is referred to as TrSVM, and can be seen as extension of support vector machines for transfer learning. Experiment results on extensive datasets show that TrSVM outperforms support vector machines and the state-of-the-art TrAdaBoost algorithm, and demonstrate the effectiveness of our algorithm.%迁移学习是对传统监督学习的扩展,试图利用其他相关领域中的现存数据来帮助完成当前领域的学习任务.对于归纳式迁移学习算法,当目标领域只有少量数据时,已有的算法容易受到选择性偏差的影响,不能充分发挥相关领域数据的作用.为解决该问题,提出一种利用领域相似性的新途径:通过定义领域弱相似性的概念,将相似性的约束与目标分类器联系起来,能在训练过程中有效利用相关领域的大量数据,设计出一种基于支持向量机的迁移学习算法TrSVM,并给出求解过程.在大量数据集上

  15. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  16. Alignment of Custom Standards by Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Adela Sirbu

    2010-09-01

    Full Text Available Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.

  17. Learning using privileged information: SVM+ and weighted SVM.

    Science.gov (United States)

    Lapin, Maksim; Hein, Matthias; Schiele, Bernt

    2014-05-01

    Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time-a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.

  18. Automated Classification of Epiphyses in the Distal Radius and Ulna using a Support Vector Machine.

    Science.gov (United States)

    Wang, Ya-hui; Liu, Tai-ang; Wei, Hua; Wan, Lei; Ying, Chong-liang; Zhu, Guang-you

    2016-03-01

    The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X-ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross-validation test and independent test for accuracy (PA ). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models.

  19. Automatic Method to Classify Images Based on Multiscale Fractal Descriptors and Paraconsistent Logic

    Science.gov (United States)

    Pavarino, E.; Neves, L. A.; Nascimento, M. Z.; Godoy, M. F.; Arruda, P. F.; Neto, D. S.

    2015-01-01

    In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Best- first Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.

  20. Application of Multidimensional Chain classifiers to Eddy Current Images for Defect Characterization

    Directory of Open Access Journals (Sweden)

    S. Shuaib Ahmed

    2012-12-01

    Full Text Available Multidimensional learning problem deals with learning a function that maps a vector of input features to a vector of class labels. Dependency between the classes is not taken into account while constructing independent classifiers for each component class of vector. To counteract this limitation, Chain Classifiers (CC approach for multidimensional learning is proposed in this study. In this approach, the information of class dependency is passed along a chain. Radial Basis Functions (RBF and Support Vector Machines (SVM are used as core for CC. Studies on multidimensional dataset of images obtained from simulated eddy current non-destructive evaluation of a stainless steel plate with sub-surface defects clearly indicate that the performance of the chain classifier is superior to the independent classifiers.

  1. Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

    Science.gov (United States)

    Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

    Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

  2. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2013-12-01

    Full Text Available The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM, combined with its sparsified version (sparse online LS-OC-SVM. LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

  3. A fast SVM training algorithm based on the set segmentation and k-means clustering

    Institute of Scientific and Technical Information of China (English)

    YANG Xiaowei; LIN Daying; HAO Zhifeng; LIANG Yanchun; LIU Guirong; HAN Xu

    2003-01-01

    At present, studies on training algorithms for support vector machines (SVM) are important issues in the field of machine learning. It is a challenging task to improve the efficiency of the algorithm without reducing the generalization performance of SVM. To face this challenge, a new SVM training algorithm based on the set segmentation and k-means clustering is presented in this paper. The new idea is to divide all the original training data into many subsets, followed by clustering each subset using k-means clustering and finally train SVM using the new data set obtained from clustering centroids. Considering that the decomposition algorithm such as SVMlight is one of the major methods for solving support vector machines, the SVMlight is used in our experiments. Simulations on different types of problems show that the proposed method can solve efficiently not only large linear classification problems but also large nonlinear ones.

  4. Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Junsheng Cheng

    2009-01-01

    Full Text Available Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD techniques, the SVD technique based on empirical mode decomposition (EMD is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

  5. 基于改进SVM的纳税评估和预测%Tax assessment and forecasting based on improved SVM

    Institute of Scientific and Technical Information of China (English)

    张一凡; 余小清; 安炫东

    2016-01-01

    At present,the tax assessment and prediction work is mainly dependent on the artificial j udgment and analysis of tax assessment personnel,which bring a large workload to tax assessment personnel and the evaluation results are not accurate.In order to solve this problem,this presents a tax assessment model based on Adaboost-PSO-SVM.It use PSO to optimize SVM weak classifier,and then utilize Adaboost to combine multiple PSO-SVM into a strong classifier for tax assessment.Experimental results show that,compared with single SVM weak classifier,the accuracy of Adaboost-PSO-SVM strong classifier is increased from 94% to 99%.On the basis of tax assessment,we apply SVM regression machine to realize the prediction of the change trend and range of variety of tax data,the results show that the prediction model contains tax assessment is better.%目前进行纳税评估和预测工作主要依赖于纳税评估人员的人工判别和分析,这样导致税务评估人员工作量较大,而且所得的评估结果也不准确。为了解决这一问题,提出了基于 Adaboost-PSO-SVM的纳税评估模型。利用PSO优化 SVM弱分类器,再用 Adaboost将多个PSO-SVM组合成为强分类器进行纳税评估。实验结果表明在纳税评估方面,相比于单个 SVM弱分类器,Adaboost-PSO-SVM强分类器的准确率由94%提高到了99%。在纳税评估的基础上,利用 SVM回归机实现对纳税数据变化趋势和变化空间的预测,结果表明包含纳税评估结果的预测模型的预测效果更好。

  6. A new damage diagnosis approach for NC machine tools based on hybrid Stationary subspace analysis

    Science.gov (United States)

    Gao, Chen; Zhou, Yuqing; Ren, Yan

    2017-05-01

    This paper focused on the damage diagnosis for NC machine tools and put forward a damage diagnosis method based on hybrid Stationary subspace analysis (SSA), for improving the accuracy and visibility of damage identification. First, the observed single sensor signal was reconstructed to multi-dimensional signals by the phase space reconstruction technique, as the inputs of SSA. SSA method was introduced to separate the reconstructed data into stationary components and non-stationary components without the need for independency and prior information of the origin signals. Subsequently, the selected non-stationary components were analysed for training LS-SVM (Least Squares Support Vector Machine) classifier model, in which several statistic parameters in the time and frequency domains were exacted as the sample of LS-SVM. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy of the proposed approach.

  7. A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

    Directory of Open Access Journals (Sweden)

    Rabia Aziz

    2016-06-01

    Full Text Available Feature (gene selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA and fuzzy backward feature elimination (FBFE are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM and Naïve Bayes (NB classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.

  8. A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients.

    Science.gov (United States)

    Candelieri, Antonio; Conforti, Domenico

    2010-07-27

    Support Vector Machines (SVMs) represent a powerful learning paradigm able to provide accurate and reliable decision functions in several application fields. In particular, they are really attractive for application in medical domain, where often a lack of knowledge exists. Kernel trick, on which SVMs are based, allows to map non-linearly separable data into potentially linearly separable one, according to the kernel function and its internal parameters value. During recent years non-parametric approaches have also been proposed for learning the most appropriate kernel, such as linear combination of basic kernels. Thus, SVMs classifiers may have several parameters to be tuned and their optimal values are usually difficult to be identified a-priori. Furthermore, combining different classifiers may reduce risk to perform errors on new unseen data. For such reasons, we present an hyper-solution framework for SVM classification, based on meta-heuristics, that searches for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration. We have applied the proposed framework on a critical and quite complex issue for the management of Chronic Heart Failure patient: the early detection of decompensation conditions. In fact, predicting new destabilizations in advance may reduce the burden of heart failure on the healthcare systems while improving quality of life of affected patients. Promising reliability has been obtained on 10-fold cross validation, proving our approach to be efficient and effective for an high-level analysis of clinical data.

  9. Genetic Algorithm with SRM SVM Classifier for Face Verification

    OpenAIRE

    Safiya K.M; Bhuvana, S.; P.TamijeSelvy; R. Radhakrishnan

    2012-01-01

    Face verification is an important problem. The problem of designing and evaluating discriminativeapproaches without explicit age modelling is used. To find the gradient orientation discard magnitudeinformation. Using hierarchical information this representation can be further improved which results inthe use of gradient orientation pyramid. When combined with a structural risk minimization support vectormachine with genetic algorithm, gradient orientation pyramid demonstrate excellent per...

  10. Fall Detector Using Discrete Wavelet Decomposition And SVM Classifier

    Directory of Open Access Journals (Sweden)

    Wójtowicz Bartłomiej

    2015-06-01

    Full Text Available This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h.

  11. Efficient iris recognition via ICA feature and SVM classifier

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    With the increasing demands of security in ournet worked society,the technology for personalidentification works as the main solution to safe-guard people s properties.Biometrics is an alterna-tive to solve the problemand has the advantage thatthey cannot be stolen or forgotten like pass words.Because personal identification numbers or identifi-cation tokens(such as s mart cards)cannot provide ahighlevel of security which can be copied,inspec-ted and/or stolen.They only showthe knowledgeof some data or belo...

  12. Empirical Analysis of Bagged SVM Classifier for Data Mining Applications

    Directory of Open Access Journals (Sweden)

    M.Govindarajan

    2013-11-01

    Full Text Available Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with as the base learner. The proposed is superior to individual approach for data mining applications in terms of classification accuracy.

  13. Weighted K-means support vector machine for cancer prediction

    OpenAIRE

    Kim, Sunghwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble tec...

  14. Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

    Directory of Open Access Journals (Sweden)

    Lin-sheng Huo

    2016-01-01

    Full Text Available An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude (CCFA with the support vector machine (SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one (OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

  15. Deriving statistical significance maps for SVM based image classification and group comparisons.

    Science.gov (United States)

    Gaonkar, Bilwaj; Davatzikos, Christos

    2012-01-01

    Population based pattern analysis and classification for quantifying structural and functional differences between diverse groups has been shown to be a powerful tool for the study of a number of diseases, and is quite commonly used especially in neuroimaging. The alternative to these pattern analysis methods, namely mass univariate methods such as voxel based analysis and all related methods, cannot detect multivariate patterns associated with group differences, and are not particularly suitable for developing individual-based diagnostic and prognostic biomarkers. A commonly used pattern analysis tool is the support vector machine (SVM). Unlike univariate statistical frameworks for morphometry, analytical tools for statistical inference are unavailable for the SVM. In this paper, we show that null distributions ordinarily obtained by permutation tests using SVMs can be analytically approximated from the data. The analytical computation takes a small fraction of the time it takes to do an actual permutation test, thereby rendering it possible to quickly create statistical significance maps derived from SVMs. Such maps are critical for understanding imaging patterns of group differences and interpreting which anatomical regions are important in determining the classifier's decision.

  16. Redundancy-Free, Accurate Analytical Center Machine for Classification

    Institute of Scientific and Technical Information of China (English)

    ZHENGFanzi; QIUZhengding; LengYonggang; YueJianhai

    2005-01-01

    Analytical center machine (ACM) has remarkable generalization performance based on analytical center of version space and outperforms SVM. From the analysis of geometry of machine learning and principle of ACM, it is showed that some training patterns are redundant to the definition of version space. Redundant patterns push ACM classifier away from analytical center of the prime version space so that the generalization performance degrades, at the same time redundant patterns slow down the classifier and reduce the efficiency of storage. Thus, an incremental algorithm is proposed to remove redundant patterns and embed into the frame of ACM that yields a Redundancy free accurate-Analytical center machine (RFA-ACM) for classification. Experiments with Heart, Thyroid, Banana datasets demonstrate the validity of RFA-ACM.

  17. Photometric classification of emission line galaxies with Machine Learning methods

    CERN Document Server

    Cavuoti, Stefano; D'Abrusco, Raffaele; Longo, Giuseppe; Paolillo, Maurizio

    2013-01-01

    In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine learning algorithms, namely the Multi Layer Perceptron (MLP), trained respectively with the Conjugate Gradient, Scaled Conjugate Gradient and Quasi Newton learning rules, and the Support Vector Machines (SVM), to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs vs non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features we discuss also the behavior of the classifiers on finer AGN classification tasks, namely Seyfert I vs Seyfert II and Seyfert vs LINER. Furthermore we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure follow...

  18. Novel cascade FPGA accelerator for support vector machines classification.

    Science.gov (United States)

    Papadonikolakis, Markos; Bouganis, Christos-Savvas

    2012-07-01

    Support vector machines (SVMs) are a powerful machine learning tool, providing state-of-the-art accuracy to many classification problems. However, SVM classification is a computationally complex task, suffering from linear dependencies on the number of the support vectors and the problem's dimensionality. This paper presents a fully scalable field programmable gate array (FPGA) architecture for the acceleration of SVM classification, which exploits the device heterogeneity and the dynamic range diversities among the dataset attributes. An adaptive and fully-customized processing unit is proposed, which utilizes the available heterogeneous resources of a modern FPGA device in efficient way with respect to the problem's characteristics. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 2-3 orders of magnitude, compared to the CPU implementation. The proposed architecture outperforms other proposed FPGA and graphic processor unit approaches by more than seven times. Furthermore, based on the special properties of the heterogeneous architecture, this paper introduces the first FPGA-oriented cascade SVM classifier scheme, which exploits the FPGA reconfigurability and intensifies the custom-arithmetic properties of the heterogeneous architecture. The results show that the proposed cascade scheme is able to increase the heterogeneous classifier throughput even further, without introducing any penalty on the resource utilization.

  19. Identification and optimization of classifier genes from multi-class earthworm microarray dataset.

    Directory of Open Access Journals (Sweden)

    Ying Li

    Full Text Available Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. First, a total of 869 genes differentially expressed in response to TNT or RDX exposure were identified using a univariate statistical algorithm of class comparison. Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance. A multiclass support vector machine (MC-SVM method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers. The combined 58 genes were considered the refined subset and used to build MC-SVM and clustering models with classification accuracy of 83.5% and 56.9%, respectively. This study demonstrates that the machine learning approach can be used to identify and optimize a small subset of classifier/biomarker genes from high dimensional datasets and generate classification models of acceptable precision for multiple classes.

  20. Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection

    Directory of Open Access Journals (Sweden)

    Wei Liu

    2014-01-01

    Full Text Available Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD techniques and support vector machines (SVMs. Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.

  1. Of Genes and Machines: application of a combination of machine learning tools to astronomy datasets

    CERN Document Server

    Heinis, S; Gezari, S; Burgett, W S; Chambers, K C; Draper, P W; Flewelling, H; Kaiser, N; Magnier, E A; Metcalfe, N; Waters, C

    2016-01-01

    We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in process of which we avoid over-fitting. We apply our method to star/galaxy separation in Pan-STARRS1 data. We show that our method correctly classifies 98% of objects down to i_P1= 24.5, with a completeness (or true positive rate) of 99% for galaxies, and 88% for stars. By combining colors with morphology, our star/classification method yields better results than the new SExtractor classifier spread_model in particular at the faint end (i_P1>22). We also use our method to derive photometric redshifts for galaxies in the COSMOS bright multi-wavelength ...

  2. FaaPred: a SVM-based prediction method for fungal adhesins and adhesin-like proteins.

    Directory of Open Access Journals (Sweden)

    Jayashree Ramana

    Full Text Available Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.

  3. 基于 RU-SMOTE-SVM 的金融市场极端风险预警研究%Research on Extreme Risk Warning for Financial Market Based on RU-SMOTE-SVM

    Institute of Scientific and Technical Information of China (English)

    林宇; 黄迅; 徐凯

    2013-01-01

      本文以上证综指和深证成指为研究对象,将随机欠采样(RU)、合成少数类过采样(SMOTE)与传统支持向量机(SVM)相结合,提出了一种改进的 SVM 模型---RU-SMOTE-SVM 模型来预测我国金融市场极端风险,并与传统 SVM、SMOTE-SVM、RU-SMOTE-NN 和 RU-SMOTE-DT 进行比较。实证结果表明,RU-SMOTE-SVM 既优于传统 SVM 模型,又比 SMOTE-SVM 具有更高的预测精度,同时还展示出比 RU-SMOTE-NN 和 RU-SMOTE-DT 更为优越的预测性能。%Taking the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index as the objects of research, this paper combines Random Under-Sampling(RU), Synthetic Minority Over-Sampling Technique (SMOTE) with Support Vector Machine ( SVM) to establish an improvement SVM---RU-SMOTE-SVM, which is applied to predict the extreme risk in Chinese financial market and compared with conventional SVM, SMOTE-SVM, RU-SMOTE-NN and RU-SMOTE-DT. The result of investigation illustrates that RU-SMOTE-SVM not only outperforms conventional SVM, but also has a higher predictive accuracy than SMOTE-SVM, simultaneously, has a more excellent predictive performance than RU-SMOTE-NN and RU-SMOTE-DT.

  4. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

    Science.gov (United States)

    Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

    2017-07-18

    Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

  5. SVM Model for Identification of human GPCRs

    CERN Document Server

    Shrivastava, Sonal; Malik, M M

    2010-01-01

    G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors in eukaryotes and they possess seven transmembrane a-helical domains. GPCRs are usually classified into several functionally distinct families that play a key role in cellular signalling and regulation of basic physiological processes. We can develop statistical models based on these common features that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. In this study, SVM based classification model has been developed for the identification of human gpcr sequences. Sequences of Level 1 subfamilies of Class A rhodopsin is considered as case study. In the present study, an attempt has been made to classify GPCRs on the basis of species. The present study classifies human gpcr sequences with rest of the species available in GPCRDB. Classification is based on specific information derived from the n-terminal and extracellular loops of the sequ...

  6. Segmentation of Magnetic Resonance Imaging MRI using LS-SVM and Wavelet Multiresolution Analysis

    Directory of Open Access Journals (Sweden)

    Luis A. Muñoz-Bedoya

    2013-11-01

    Full Text Available Currently, support vector machines (SVM have become a powerful tool to solve nonlinear classification problems. For the optimization of the tool, has developed a reformulation known as LS-SVM (Support Vector Machine least squares, which works with a model based on function minimization and Lagrange polynomials. Therefore, this paper presents a method for segmentation of magnetic resonance images specifically to study the morphology of the lungs and reach the quantification of relevant features in these images using SVM and LS-SVM. In addition to sorting technique in this work using techniques such as wavelet analysis to eliminate irrelevant information (compression and Splines algorithms to interpolate the information found and quantify the characteristics, which in this work were based on the recognition area, shape and abnormal structures present in the lung of these images.

  7. Evaluation of the Vocal Tract Length Normalization Based Classifiers for Speaker Verification

    Directory of Open Access Journals (Sweden)

    Walid Hussein

    2016-12-01

    Full Text Available This paper proposes and evaluates classifiers based on Vocal Tract Length Normalization (VTLN in a text-dependent speaker verification (SV task with short testing utterances. This type of tasks is important in commercial applications and is not easily addressed with methods designed for long utterances such as JFA and i-Vectors. In contrast, VTLN is a speaker compensation scheme that can lead to significant improvements in speech recognition accuracy with just a few seconds of speech samples. A novel scheme to generate new classifiers is employed by incorporating the observation vector sequence compensated with VTLN. The modified sequence of feature vectors and the corresponding warping factors are used to generate classifiers whose scores are combined by a Support Vector Machine (SVM based SV system. The proposed scheme can provide an average reduction in EER equal to 14% when compared with the baseline system based on the likelihood of observation vectors.

  8. Power quality events recognition using a SVM-based method

    Energy Technology Data Exchange (ETDEWEB)

    Cerqueira, Augusto Santiago; Ferreira, Danton Diego; Ribeiro, Moises Vidal; Duque, Carlos Augusto [Department of Electrical Circuits, Federal University of Juiz de Fora, Campus Universitario, 36036 900, Juiz de Fora MG (Brazil)

    2008-09-15

    In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost. (author)

  9. Incremental Learning with SVM for Multimodal Classification of Prostatic Adenocarcinoma

    OpenAIRE

    José Fernando García Molina; Lei Zheng; Metin Sertdemir; Dietmar J Dinter; Stefan Schönberg; Matthias Rädle

    2014-01-01

    Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correcti...

  10. 基于支持向量机逼近的内模控制系统及应用%SVM Approximate-based Internal Model Control Strategy

    Institute of Scientific and Technical Information of China (English)

    王耀南; 袁小芳

    2008-01-01

    A support vector machine (SVM) approximate-based internal model control (IMC) strategy is presented for the steam valving control of synchronous generators. The proposed SVM IMC strategy includes two main parts: SVM approximate inverse controller and uncertainty compensation in the internal model structure. The SVM inverse controller is derived directly using an input-output approximation approach via Taylor expansion, and it is implemented through nonlinear system identification without further online training. Frthermore, a robustness filter is used for uncertainty compensation in the internal model structure.Simulations show the effectiveness of the SVM IMC strategy for the steam valving control.

  11. Comparative analysis of classifiers for developing an adaptive computer-assisted EEG analysis system for diagnosing epilepsy.

    Science.gov (United States)

    Ahmad, Malik Anas; Ayaz, Yasar; Jamil, Mohsin; Omer Gillani, Syed; Rasheed, Muhammad Babar; Imran, Muhammad; Khan, Nadeem Ahmed; Majeed, Waqas; Javaid, Nadeem

    2015-01-01

    Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system's incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.

  12. SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

    Institute of Scientific and Technical Information of China (English)

    钟伟民; 何国龙; 皮道映; 孙优贤

    2005-01-01

    A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

  13. Study on phase retardation characteristic of LCVR using dispersion analysis and SVM

    Institute of Scientific and Technical Information of China (English)

    HU; Dongmei; LIU; Quan; NIU; Guocheng; ZHU; Yifeng; YU; Lintao

    2015-01-01

    To calibrate the phase retardance of a Liquid crystal variable retarder(LCVR),its birefringence dispersion characteristic was analyzed,and the Support vector machines(SVM) algorithm was adopted to establish the prediction model.The obtained SVM decision function was used as a part of LCVR phase retardance,which was generated by the driving voltage.The experimental verification was carried out with a 568 nm laser.The results show that the deviation of the experimental value and the theoretical value is about 0.0061λ.SVM method could be used as an effective method for LCVR phase retardance characteristic calibration.

  14. Credit risk evaluation using adaptive Lq penalty SVM with Gauss kernel

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensio...

  15. Identification of eggs from different production systems based on hyperspectra and CS-SVM.

    Science.gov (United States)

    Sun, J; Cong, S; Mao, H; Zhou, X; Wu, X; Zhang, X

    2017-01-19

    1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables (SNV) combined with Savitzky-Golay (SG) were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) to establish a SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

  16. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  17. Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images.

    Science.gov (United States)

    Gopinath, Balasubramanian; Shanthi, Natesan

    2013-06-01

    An automated computer-aided diagnosis system is developed to classify benign and malignant thyroid nodules using multi-stained fine needle aspiration biopsy (FNAB) cytological images. In the first phase, the image segmentation is performed to remove the background staining information and retain the appropriate foreground cell objects in cytological images using mathematical morphology and watershed transform segmentation methods. Subsequently, statistical features are extracted using two-level discrete wavelet transform (DWT) decomposition, gray level co-occurrence matrix (GLCM) and Gabor filter based methods. The classifiers k-nearest neighbor (k-NN), Elman neural network (ENN) and support vector machine (SVM) are tested for classifying benign and malignant thyroid nodules. The combination of watershed segmentation, GLCM features and k-NN classifier results a lowest diagnostic accuracy of 60 %. The highest diagnostic accuracy of 93.33 % is achieved by ENN classifier trained with the statistical features extracted by Gabor filter bank from the images segmented by morphology and watershed transform segmentation methods. It is also observed that SVM classifier results its highest diagnostic accuracy of 90 % for DWT and Gabor filter based features along with morphology and watershed transform segmentation methods. The experimental results suggest that the developed system with multi-stained thyroid FNAB images would be useful for identifying thyroid cancer irrespective of staining protocol used.

  18. Building Support Vector Machine with Reduced Feature Complexity%构造特征复杂性减低的支持向量机

    Institute of Scientific and Technical Information of China (English)

    宇缨

    2007-01-01

    支持向量机(SVM)较一般的机器学习方法显示出更好的泛化能力.然而,在实际的数据中经常存在着大量冗余、噪声或者不可靠的特征,这严重影响到SVM的性能.因此,有必要减低特征复杂性以获取更好的SVM结果.本文提出了一种基于遗传算法(GA)的嵌入式框架下的特征优化算法,以构造改进SVM.针对选择的UCI成人数据库的实验表明,与原始的SVM相比,提出的改进SVM方法获得了更少的支持向量数目和更好的分类精度.%Support Vector Machine (SVM) has revealed better generalization than conventional machine learning methods. However, in the real data there often exist a large number of redundant, noisy or unreliable features to deteriorate the function of SVM strongly. So to reduce the feature complexity, it is necessary to improve the performance of SVM for better results. A method to build modified SVM, which is based on embedded methods for feature optimization using Genetic Algorithm (GA),is proposed in this paper. The experimental results on selected UCI Adult data base show that compared with original SVM classifier, the number of support vector decreases and better classification results are achieved based on our modified SVM.

  19. Application of HMM-SVM in Fault Diagnosis of Analog Circuits%HMM-SVM混合模型在模拟电路故障诊断中的应用

    Institute of Scientific and Technical Information of China (English)

    刘任洋; 吴文全; 李超; 马龙

    2013-01-01

    针对单一的隐马尔科夫模型(HMM)或支持向量机(SVM)在模拟电路早期的软故障中识别率不高的特点,将HMM-SVM混合模型应用到模拟电路早期的软故障识别中.首先通过主成分分析(PCA)将原始数据样本降维实现初步划分;接着利用HMM计算测试样本与各故障状态的匹配程度形成特征向量;最后由SVM做故障状态判别.实验结果表明,HMM-SVM混合模型的早期故障识别率优于单一的HMM或SVM模型,将平均故障识别率提高到95%以上.%Since the incipient faults of analog circuit are hard to be identified well by using only Hidden Markov Model (HMM) or Support Vector Machine (SVM), a new fault diagnosis method based on HMM-SVM was proposed. Firstly, the dimensions of the experimental samples were decreased and classified briefly by Principal Components Analysis (PCA). Then, HMM was used to calculate the matching degree between the test samples and all the fault states, which formed the feature vectors for SVM in final diagnosis. The result shows that HMM-SVM is better than single HMM or SVM model for the incipient fault diagnosis, and the average fault recognition rate was increased by more than ninety-five percent.

  20. A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

    Science.gov (United States)

    Zhan, Liwei; Li, Chengwei

    2017-02-01

    A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

  1. A Semisupervised Support Vector Machines Algorithm for BCI Systems

    Directory of Open Access Journals (Sweden)

    Jianzhao Qin

    2007-07-01

    Full Text Available As an emerging technology, brain-computer interfaces (BCIs bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM algorithm for brain-computer interface (BCI systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.

  2. Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

    Science.gov (United States)

    Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

    2016-09-01

    Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

  3. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  4. Support vector machine classification trees based on fuzzy entropy of classification.

    Science.gov (United States)

    de Boves Harrington, Peter

    2017-02-15

    The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). This classifier partitioned the data by finding gaps in the data space. For large and complex datasets, there may be no gaps in the data space confounding this type of classifier. A novel algorithm was devised that uses fuzzy entropy to find optimal partitions for situations when clusters of data are overlapped in the data space. Also, a kernel version of the fuzzy entropy algorithm was devised. A fast support vector machine implementation is used that has no cost C or slack variables to optimize. Statistical comparisons using bootstrapped Latin partitions among the tree classifiers were made using a synthetic XOR data set and validated with ten prediction sets comprised of 50,000 objects and a data set of NMR spectra obtained from 12 tea sample extracts.

  5. Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    TIAN Huifang; SUN Shanxia

    2006-01-01

    By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.

  6. Accuracy comparison among different machine learning techniques for detecting malicious codes

    Science.gov (United States)

    Narang, Komal

    2016-03-01

    In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

  7. Discrimination of Rice Varieties using LS-SVM Classification Algorithms and Hyperspectral Data

    Directory of Open Access Journals (Sweden)

    Jin Xiaming

    2015-03-01

    Full Text Available Fast discrimination of rice varieties plays a key role in the rice processing industry and benefits the management of rice in the supermarket. In order to discriminate rice varieties in a fast and nondestructive way, hyperspectral technology and several classification algorithms were used in this study. The hyperspectral data of 250 rice samples of 5 varieties were obtained using FieldSpec®3 spectrometer. Multiplication Scatter Correction (MSC was used to preprocess the raw spectra. Principal Component Analysis (PCA was used to reduce the dimension of raw spectra. To investigate the influence of different linear and non-linear classification algorithms on the discrimination results, K-Nearest Neighbors (KNN, Support Vector Machine (SVM and Least Square Support Vector Machine (LS-SVM were used to develop the discrimination models respectively. Then the performances of these three multivariate classification methods were compared according to the discrimination accuracy. The number of Principal Components (PCs and K parameter of KNN, kernel function of SVM or LS-SVM, were optimized by cross-validation in corresponding models. One hundred and twenty five rice samples (25 of each variety were chosen as calibration set and the remaining 125 rice samples were prediction set. The experiment results showed that, the optimal PCs was 8 and the cross-validation accuracy of KNN (K = 2, SVM, LS-SVM were 94.4, 96.8 and 100%, respectively, while the prediction accuracy of KNN (K = 2, SVM, LS-SVM were 89.6, 93.6 and 100%, respectively. The results indicated that LS-SVM performed the best in the discrimination of rice varieties.

  8. Human action recognition with group lasso regularized-support vector machine

    Science.gov (United States)

    Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

    2016-05-01

    The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

  9. An Automated Recognition of Fake or Destroyed Indian Currency Notes in Machine Vision

    Directory of Open Access Journals (Sweden)

    Sanjana

    2012-04-01

    Full Text Available Almost every country in the world face the problem of counterfeitcurrency notes, but in India the problem is acute as the country ishit hard by this evil practice. Fake notes in India in denominationsof Rs.100, 500 and 1000 are being flooded into the system. Inorder to deal with such type of problems, an automated recognitionof currency notes in introduced by with the help of featureextraction, classification based in SVM, Neural Nets, and heuristicapproach. This technique is also subjected with the computervision where all processing with the image is done by machine.The machine is fitted with a CDD camera which will scan theimage of the currency note considering the dimensions of thebanknote and software will process the image segments with thehelp of SVM and character recognition methods. ANN is alsointroduced in this paper to train the data and classify the segmentsusing its datasets. To implement this design we are dealing withMATLAB Tool.

  10. Recursive support vector machines for dimensionality reduction.

    Science.gov (United States)

    Tao, Qing; Chu, Dejun; Wang, Jue

    2008-01-01

    The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.

  11. Performance and optimization of support vector machines in high-energy physics classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, Mehmet Oezguer; Kruecker, Dirk; Melzer-Pellmann, Isabell [DESY, Hamburg (Germany)

    2016-07-01

    In this talk, the use of Support Vector Machines (SVM) is promoted for new-physics searches in high-energy physics. We developed an interface, called SVM HEP Interface (SVM-HINT), for a popular SVM library, LibSVM, and introduced a statistical-significance based hyper-parameter optimization algorithm for the new-physics searches. As example case study, a search for Supersymmetry at the Large Hadron Collider is given to demonstrate the capabilities of SVM using SVM-HINT.

  12. Support Vector Machine Using Multiple Hyperplanes for Rare Class%一种针对稀有类支持向量机的新算法

    Institute of Scientific and Technical Information of China (English)

    徐凤芳; 高卫国

    2013-01-01

    Support vector machine is a very popular method to classify data from different classes. But it doesn't work well when the number of data in one of the classes is very rare. A new method called Support Vector Machine using Multiple Hyperplanes(SVMMH) will be introduced. Instead of using one hyperplane to classify two classes of data, SVMMH uses multiple hyperplanes. All the data in the rare class have to be on the side of the hyperplanes that are parallel to the normal vectors. The classifier of the new method is more strict than that of SVM. SVM is also a SVM_MH method with only one hyperplane. Kernels can be used in the new method.%支持向量机方法是流行的数据分类方法,但支持向量机方法对稀有类的分类能力不强.针对稀有类数据的多超平面支持向量机是一种基于支持向量机方法的稀有点类分类方法,与支持向量机相似,使用超平面进行分类.与支持向量机不同的是,SVM_MH要求稀有类点在所有超平面正侧的交集中.SVM_MH对稀有类的分类要求更严格,而对非稀有类的条件相对宽松.支持向量机方法可以看作是一个特殊的SVM_MH.核函数在稀有类支持向量机中仍然适用.

  13. Performance and optimization of support vector machines in high-energy physics classification problems

    Science.gov (United States)

    Sahin, M. Ö.; Krücker, D.; Melzer-Pellmann, I.-A.

    2016-12-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.

  14. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  15. SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA.

    Science.gov (United States)

    López, M M; Ramírez, J; Górriz, J M; Alvarez, I; Salas-Gonzalez, D; Segovia, F; Chaves, R

    2009-10-30

    Single-photon emission tomography (SPECT) imaging has been widely used to guide clinicians in the early Alzheimer's disease (AD) diagnosis challenge. However, AD detection still relies on subjective steps carried out by clinicians, which entail in some way subjectivity to the final diagnosis. In this work, kernel principal component analysis (PCA) and linear discriminant analysis (LDA) are applied on functional images as dimension reduction and feature extraction techniques, which are subsequently used to train a supervised support vector machine (SVM) classifier. The complete methodology provides a kernel-based computer-aided diagnosis (CAD) system capable to distinguish AD from normal subjects with 92.31% accuracy rate for a SPECT database consisting of 91 patients. The proposed methodology outperforms voxels-as-features (VAF) that was considered as baseline approach, which yields 80.22% for the same SPECT database.

  16. A support vector machine approach for classification of welding defects from ultrasonic signals

    Science.gov (United States)

    Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

    2014-07-01

    Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

  17. Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jianwei Cui

    2014-01-01

    Full Text Available This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB with support vector machine (SVM. The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT- based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

  18. Classifying Multi-year Land Use and Land Cover using Deep Convolutional Neural Networks

    Science.gov (United States)

    Seo, B.

    2015-12-01

    Cultivated ecosystems constitute a particularly frequent form of human land use. Long-term management of a cultivated ecosystem requires us to know temporal change of land use and land cover (LULC) of the target system. Land use and land cover changes (LUCC) in agricultural ecosystem is often rapid and unexpectedly occurs. Thus, longitudinal LULC is particularly needed to examine trends of ecosystem functions and ecosystem services of the target system. Multi-temporal classification of land use and land cover (LULC) in complex heterogeneous landscape remains a challenge. Agricultural landscapes often made up of a mosaic of numerous LULC classes, thus spatial heterogeneity is large. Moreover, temporal and spatial variation within a LULC class is also large. Under such a circumstance, standard classifiers would fail to identify the LULC classes correctly due to the heterogeneity of the target LULC classes. Because most standard classifiers search for a specific pattern of features for a class, they fail to detect classes with noisy and/or transformed feature data sets. Recently, deep learning algorithms have emerged in the machine learning communities and shown superior performance on a variety of tasks, including image classification and object recognition. In this paper, we propose to use convolutional neural networks (CNN) to learn from multi-spectral data to classify agricultural LULC types. Based on multi-spectral satellite data, we attempted to classify agricultural LULC classes in Soyang watershed, South Korea for the three years' study period (2009-2011). The classification performance of support vector machine (SVM) and CNN classifiers were compared for different years. Preliminary results demonstrate that the proposed method can improve classification performance compared to the SVM classifier. The SVM classifier failed to identify classes when trained on a year to predict another year, whilst CNN could reconstruct LULC maps of the catchment over the study

  19. Classification of hydration status using electrocardiogram and machine learning

    Science.gov (United States)

    Kaveh, Anthony; Chung, Wayne

    2013-10-01

    The electrocardiogram (ECG) has been used extensively in clinical practice for decades to non-invasively characterize the health of heart tissue; however, these techniques are limited to time domain features. We propose a machine classification system using support vector machines (SVM) that uses temporal and spectral information to classify health state beyond cardiac arrhythmias. Our method uses single lead ECG to classify volume depletion (or dehydration) without the lengthy and costly blood analysis tests traditionally used for detecting dehydration status. Our method builds on established clinical ECG criteria for identifying electrolyte imbalances and lends to automated, computationally efficient implementation. The method was tested on the MIT-BIH PhysioNet database to validate this purely computational method for expedient disease-state classification. The results show high sensitivity, supporting use as a cost- and time-effective screening tool.

  20. [MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

    Science.gov (United States)

    Chen, Zhiru; Hong, Wenxue

    2016-02-01

    Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

  1. The use of machine learning with signal- and NLP processing of source code to detect and classify vulnerabilities and weaknesses with MARFCAT

    CERN Document Server

    Mokhov, Serguei A

    2010-01-01

    We present a machine learning approach to static code analysis for weaknesses related to security and others with the open-source MARF framework and its application to for the NIST's SATE 2010 static analysis tool exhibition workshop.

  2. Image Segmentation Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    XU Hai-xiang; ZHU Guang-xi; TIAN Jin-wen; ZHANG Xiang; PENG Fu-yuan

    2005-01-01

    Image segmentation is a necessary step in image analysis. Support vector machine (SVM) approach is proposed to segment images and its segmentation performance is evaluated.Experimental results show that: the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron (BP-MLP) approach and fuzzy c-means (FCM) approach.

  3. Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine.

    Science.gov (United States)

    Cao, Cheng; Tutwiler, Richard Laurence; Slobounov, Semyon

    2008-08-01

    There is a growing body of knowledge indicating long-lasting residual electroencephalography (EEG) abnormalities in concussed athletes that may persist up to 10-year postinjury. Most often, these abnormalities are initially overlooked using traditional concussion assessment tools. Accordingly, premature return to sport participation may lead to recurrent episodes of concussion, increasing the risk of recurrent concussions with more severe consequences. Sixty-one athletes at high risk for concussion (i.e., collegiate rugby and football players) were recruited and underwent EEG baseline assessment. Thirty of these athletes suffered from concussion and were retested at day 30 postinjury. A number of task-related EEG recordings were conducted. A novel classification algorithm, the support vector machine (SVM), was applied as a classifier to identify residual functional abnormalities in athletes suffering from concussion using a multichannel EEG data set. The total accuracy of the classifier using the 10 features was 77.1%. The classifier has a high sensitivity of 96.7% (linear SVM), 80.0% (nonlinear SVM), and a relatively lower but acceptable selectivity of 69.1% (linear SVM) and 75.0% (nonlinear SVM). The major findings of this report are as follows: 1) discriminative features were observed at theta, alpha, and beta frequency bands, 2) the minimal redundancy relevance method was identified as being superior to the univariate t -test method in selecting features for the model calculation, 3) the EEG features selected for the classification model are linked to temporal and occipital areas, and 4) postural parameters influence EEG data set and can be used as discriminative features for the classification model. Overall, this report provides sufficient evidence that 10 EEG features selected for final analysis and SVM may be potentially used in clinical practice for automatic classification of athletes with residual brain functional abnormalities following a concussion

  4. Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

    Institute of Scientific and Technical Information of China (English)

    Wang Hongjun; Xu Xiaoli; Rosen B G

    2014-01-01

    Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine (PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

  5. Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines.

    Science.gov (United States)

    Kyrkou, Christos; Bouganis, Christos-Savvas; Theocharides, Theocharis; Polycarpou, Marios M

    2016-01-01

    Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the majority of the data belong to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, SVM classification is a computationally demanding task and existing hardware architectures for SVMs only consider monolithic classifiers. This paper proposes the acceleration of cascade SVMs through a hybrid processing hardware architecture optimized for the cascade SVM classification flow, accompanied by a method to reduce the required hardware resources for its implementation, and a method to improve the classification speed utilizing cascade information to further discard data samples. The proposed SVM cascade architecture is implemented on a Spartan-6 field-programmable gate array (FPGA) platform and evaluated for object detection on 800×600 (Super Video Graphics Array) resolution images. The proposed architecture, boosted by a neural network that processes cascade information, achieves a real-time processing rate of 40 frames/s for the benchmark face detection application. Furthermore, the hardware-reduction method results in the utilization of 25% less FPGA custom-logic resources and 20% peak power reduction compared with a baseline implementation.

  6. Stellar Spectral Classification with Locality Preserving Projections and Support Vector Machine

    Indian Academy of Sciences (India)

    Liu Zhong-bao

    2016-06-01

    With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) is widely used in stellar spectra classification. Another dimensionality reduction technique, Locality Preserving Projections (LPP) has not been widely used in astronomy. The advantage of LPP is that it can preserve the local structure of the data after dimensionality reduction. In view of this, we investigate how to apply LPP+SVM in classifying the stellar spectral subclasses. In the comparative experiment, the performance of LPP is compared with PCA. The stellar spectral classification process is composed of the following steps. Firstly, PCA and LPP are respectively applied to reduce the dimension of spectra data. Then, Support Vector Machine (SVM) is used to classify the 4 subclasses of K-type and 3 subclasses of F-type spectra from Sloan Digital Sky Survey (SDSS). Lastly, the performance of LPP+SVM is compared with that of PCA+SVM in stellar spectral classification, and we found that LPP does better than PCA.

  7. Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis

    Science.gov (United States)

    Rostami, Alireza; Masoudi, Mohammad; Ghaderi-Ardakani, Alireza; Arabloo, Milad; Amani, Mahmood

    2016-06-01

    Among the most significant physical characteristics of porous media, the effective thermal conductivity (ETC) is used for estimating the thermal enhanced oil recovery process efficiency, hydrocarbon reservoir thermal design, and numerical simulation. This paper reports the implementation of an innovative least square support vector machine (LS-SVM) algorithm for the development of enhanced model capable of predicting the ETCs of dry sandstones. By means of several statistical parameters, the validity of the presented model was evaluated. The prediction of the developed model for determining the ETCs of dry sandstones was in excellent agreement with the reported data with a coefficient of determination value ({R}2) of 0.983 and an average absolute relative deviation of 0.35 %. Results from present research show that the proposed LS-SVM model is robust, reliable, and efficient in calculating the ETCs of sandstones.

  8. SVM for density estimation and application to medical image segmentation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhao; ZHANG Su; ZHANG Chen-xi; CHEN Ya-zhu

    2006-01-01

    A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process.Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

  9. Using SVM to construct a Chinese dependency parser

    Institute of Scientific and Technical Information of China (English)

    XU Yun; ZHANG Feng

    2006-01-01

    In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated.Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.

  10. Efficient SVM-based Recognition of Chinese Personal Names

    Institute of Scientific and Technical Information of China (English)

    Yu Ying(宇缨); Wang Xiaolong; Liu Bingquan; Wang Hui

    2004-01-01

    This paper provides a flexible and efficient method to identify Chinese personal names based on SVM (Support Vector Machines). In its approach, forming rules of personal name is employed to select candidate set, then SVM based identification strategies is used to recognize real personal name in the candidate set. Basic semanteme of word in context and frequency information of word inside candidate are selected as features in its methodology, which reduce the feature space scale dramatically and calculate more efficiently. Results of open testing achieved F-measure 90.59% in 2 million words news and F-measure 86.67% in 16.17 million words news based on this project.

  11. A novel transmission line protection using DOST and SVM

    Directory of Open Access Journals (Sweden)

    M. Jaya Bharata Reddy

    2016-06-01

    Full Text Available This paper proposes a smart fault detection, classification and location (SFDCL methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST. The methodology is based on synchronized current measurements from remote telemetry units (RTUs installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS and artificial neural network (ANN, for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.

  12. Gazing-detection of human eyes based on SVM

    Institute of Scientific and Technical Information of China (English)

    LI Su-mei; ZHANG Yan-xin; CHANG Sheng-jiang; SHEN Jin-yuan

    2005-01-01

    A method for gazing-detection of human eyes using Support Vector Machine (SVM) based on statistic learning theory (SLT) is proposed.According to the criteria of structural risk minimization of SVM,the errors between sample-data and model-data are minimized and the upper bound of predicting error of the model is also reduced.As a result,the generalization ability of the model is much improved.The simulation results show that,when limited training samples are used,the correct recognition rate of the tested samples can be as high as 100%,which is much better than some previous results obtained by other methods.The higher processing speed enables the system to distinguish gazing or not-gazing in real-time.

  13. SVM-based glioma grading: Optimization by feature reduction analysis.

    Science.gov (United States)

    Zöllner, Frank G; Emblem, Kyrre E; Schad, Lothar R

    2012-09-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity=89%, specificity=84%) when reducing the feature vector from 101 (100-bins rCBV histogram+age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∼87%) while reducing the number of features by up to 98%.

  14. Quality-Oriented Classification of Aircraft Material Based on SVM

    Directory of Open Access Journals (Sweden)

    Hongxia Cai

    2014-01-01

    Full Text Available The existing material classification is proposed to improve the inventory management. However, different materials have the different quality-related attributes, especially in the aircraft industry. In order to reduce the cost without sacrificing the quality, we propose a quality-oriented material classification system considering the material quality character, Quality cost, and Quality influence. Analytic Hierarchy Process helps to make feature selection and classification decision. We use the improved Kraljic Portfolio Matrix to establish the three-dimensional classification model. The aircraft materials can be divided into eight types, including general type, key type, risk type, and leveraged type. Aiming to improve the classification accuracy of various materials, the algorithm of Support Vector Machine is introduced. Finally, we compare the SVM and BP neural network in the application. The results prove that the SVM algorithm is more efficient and accurate and the quality-oriented material classification is valuable.

  15. An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

    Directory of Open Access Journals (Sweden)

    Safat B. Wali

    2015-01-01

    Full Text Available The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%, and processing time (0.43 s. The area under the receiver operating characteristic (ROC curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

  16. Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines

    Directory of Open Access Journals (Sweden)

    Li FuFeng

    2012-08-01

    Full Text Available Abstract Background In Traditional Chinese Medicine (TCM, the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor’s nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor’s experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. Methods A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination, mRMR (minimum Redundancy Maximum Relevance and IG (information gain. Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM. In addition, we compare SVM with k-nearest neighbor (kNN algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. Results A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The

  17. Support Vector Machine Ensemble Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Ye; YIN Ru-po; CAI Yun-ze; XU Xiao-ming

    2006-01-01

    Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

  18. Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking.

    Science.gov (United States)

    Yin, Yingjie; Xu, De; Wang, Xingang; Bai, Mingran

    2015-09-01

    In this paper, we propose a robust state-based structured support vector machine (SVM) tracking algorithm combined with incremental principal component analysis (PCA). Different from the current structured SVM for tracking, our method directly learns and predicts the object's states and not the 2-D translation transformation during tracking. We define the object's virtual state to combine the state-based structured SVM and incremental PCA. The virtual state is considered as the most confident state of the object in every frame. The incremental PCA is used to update the virtual feature vector corresponding to the virtual state and the principal subspace of the object's feature vectors. In order to improve the accuracy of the prediction, all the feature vectors are projected onto the principal subspace in the learning and prediction process of the state-based structured SVM. Experimental results on several challenging video sequences validate the effectiveness and robustness of our approach.

  19. SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hua; WANG Yun-jia; LI Yong-feng

    2009-01-01

    A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed. Based on least squares support vector machine (LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction. Many of the geological and mining factors involved are related in a nonlinear way. The new model is based on statistical theory (SLT) and empirical risk minimization (ERM) principles. Typical data collected from observation stations were used for the learning and training samples. The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network (BPNN) model. The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model. The LS-SVM model was faster in computation and had better generalized performance. It provides a highly effective method for calculating the predicting parameters of the probability-integral method.

  20. Application of SVM in Analyzing the Headstream of Gushing Water in Coal Mine

    Institute of Scientific and Technical Information of China (English)

    YAN Zhi-gang; ZHANG Hai-rong; DU Pei-jun

    2006-01-01

    To recognize the presence of the headstream of gushing water in coal mines, the SVM (Support Vector Machine) was proposed to analyze the gushing water based on hydrogeochemical methods. First, the SVM model for headstream analysis was trained on the water sample of available headstreams, and then we used this to predict the unknown samples, which were validated in practice by comparing the predicted results with the actual results. The experimental results show that the SVM is a feasible method to differentiate between two headstreams and the H-SVMs (Hierachical SVMs) is a preferable way to deal with the problem of multi-headstreams. Compared with other methods, the SVM is based on a strict mathematical theory with a simple structure and good generalization properties. As well, the support vector W in the decision function can describe the weights of the recognition factors of water samples, which is very important for the analysis of headstreams of gushing water in coal mines.

  1. Fault diagnosis of a mine hoist using PCA and SVM techniques

    Institute of Scientific and Technical Information of China (English)

    CHANG Yan-wei; WANG Yao-cai; LIU Tao; WANG Zhi-jie

    2008-01-01

    A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.

  2. Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

    Directory of Open Access Journals (Sweden)

    Zhou Wan

    2015-01-01

    Full Text Available Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM. After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV. After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.

  3. Comparison of Advanced Pixel Based (ANN and SVM and Object-Oriented Classification Approaches Using Landsat-7 Etm+ Data

    Directory of Open Access Journals (Sweden)

    Prasun Kumar Gupta

    2010-08-01

    Full Text Available In this study, the pixel-based and object-oriented image classification approaches were used for identifying different land use types in Karnal district. Imagery from Landsat-7 ETM with 6 spectral bands was used to perform the image classification.Ground truth data were collected from the available maps, personal knowledge and communication with the local people. In order to prepare land use map different approaches: Artificial Neural Network(ANN and Support Vector Machine (SVM were used. For performing object oriented classification eCognition software was used. During the object oriented classification, in first step several differentsets of parameters were used for image segmentation and in second step nearest neighbor classifier was used for classification. Outcome from the classification works show that the object-oriented approach gave more accurate results (including higher producer’s and user’s accuracy for most of the land cover classes than those achieved by pixelbased classification algorithms. It is also observed that ANN performed better as compared to SVM classification approach.

  4. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

    Science.gov (United States)

    Lu, Huiling; Zhang, Junjie; Shi, Hongbin

    2016-01-01

    In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.

  5. A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

    Directory of Open Access Journals (Sweden)

    B. Yekkehkhany

    2014-10-01

    Full Text Available In this paper, a framework is developed based on Support Vector Machines (SVM for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF. The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  6. Clustering technique-based least square support vector machine for EEG signal classification.

    Science.gov (United States)

    Siuly; Li, Yan; Wen, Peng Paul

    2011-12-01

    This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

  7. Oil spill detection from SAR image using SVM based classification

    Directory of Open Access Journals (Sweden)

    A. A. Matkan

    2013-09-01

    Full Text Available In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results.

  8. MISR Level 2 TOA/Cloud Classifier parameters (MIL2TCCL_V2)

    Science.gov (United States)

    Diner, David J. (Principal Investigator)

    The TOA/Cloud Classifiers contain the Angular Signature Cloud Mask (ASCM), a scene classifier calculated using support vector machine technology (SVM) both of which are on a 1.1 km grid, and cloud fractions at 17.6 km resolution that are available in different height bins (low, middle, high) and are also calculated on an angle-by-angle basis. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=17.6 km; Longitude_Resolution=17.6 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, Daily - < Weekly].

  9. MISR Level 2 TOA/Cloud Classifier parameters (MIL2TCCL_V3)

    Science.gov (United States)

    Diner, David J. (Principal Investigator)

    The TOA/Cloud Classifiers contain the Angular Signature Cloud Mask (ASCM), a scene classifier calculated using support vector machine technology (SVM) both of which are on a 1.1 km grid, and cloud fractions at 17.6 km resolution that are available in different height bins (low, middle, high) and are also calculated on an angle-by-angle basis. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1.1 km; Longitude_Resolution=1.1 km; Temporal_Resolution=about 15 orbits/day].

  10. Support Vector Machine%支持向量机

    Institute of Scientific and Technical Information of China (English)

    张浩然; 韩正之; 李昌刚

    2002-01-01

    This paper gives a introduction of the basic ideas, basic theory, key techniques, and application of the sup-port vector machine (SVM), and indicates the similarities and differences between support vector machines and neuralnetworks.

  11. Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

    Science.gov (United States)

    Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

    2015-01-01

    This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

  12. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    Science.gov (United States)

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  13. SVM Intrusion Detection Model Based on Compressed Sampling

    Directory of Open Access Journals (Sweden)

    Shanxiong Chen

    2016-01-01

    Full Text Available Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.

  14. Efficient Multiplicative Updates for Support Vector Machines

    DEFF Research Database (Denmark)

    Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten

    2009-01-01

    The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...

  15. Efficient Multiplicative Updates for Support Vector Machines

    DEFF Research Database (Denmark)

    Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten

    2009-01-01

    The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...

  16. An Optional Threshold with Svm Cloud Detection Algorithm and Dsp Implementation

    Science.gov (United States)

    Zhou, Guoqing; Zhou, Xiang; Yue, Tao; Liu, Yilong

    2016-06-01

    This paper presents a method which combines the traditional threshold method and S