21 CFR 1.406 - How will FDA handle classified information in an informal hearing?

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 1 2010-04-01 2010-04-01 false How will FDA handle classified information in an informal hearing? 1.406 Section 1.406 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL GENERAL ENFORCEMENT REGULATIONS Administrative Detention of Food for Human or...

A cascade of classifiers for extracting medication information from discharge summaries

Directory of Open Access Journals (Sweden)

Halgrim Scott

2011-07-01

Full Text Available Abstract Background Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task. Methods We present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events. Results The system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists. Conclusions This study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.

[Application of classified protection of information security in the information system of air pollution and health impact monitoring].

Science.gov (United States)

Hao, Shuxin; Lü, Yiran; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To study the application of classified protection of information security in the information system of air pollution and health impact monitoring, so as to solve the possible safety risk of the information system. According to the relevant national standards and requirements for the information system security classified protection, and the professional characteristics of the information system, to design and implement the security architecture of information system, also to determine the protection level of information system. Basic security measures for the information system were developed in the technical safety and management safety aspects according to the protection levels, which effectively prevented the security risk of the information system. The information system established relatively complete information security protection measures, to enhanced the security of professional information and system service, and to ensure the safety of air pollution and health impact monitoring project carried out smoothly.

Comparison of classifiers for decoding sensory and cognitive information from prefrontal neuronal populations.

Directory of Open Access Journals (Sweden)

Elaine Astrand

Full Text Available Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF: the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.

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

48 CFR 952.204-76 - Conditional payment of fee or profit-safeguarding restricted data and other classified information.

Science.gov (United States)

2010-10-01

... disclosure of Top Secret Restricted Data or other information classified as Top Secret, any classification... result in the loss, compromise, or unauthorized disclosure of Top Secret Restricted Data, or other information classified as Top Secret, any classification level of information in a SAP, information identified...

New approach to information fusion for Lipschitz classifiers ensembles: Application in multi-channel C-OTDR-monitoring systems

Energy Technology Data Exchange (ETDEWEB)

Timofeev, Andrey V.; Egorov, Dmitry V. [LPP “EqualiZoom”, Astana, 010000 (Kazakhstan)

2016-06-08

This paper presents new results concerning selection of an optimal information fusion formula for an ensemble of Lipschitz classifiers. The goal of information fusion is to create an integral classificatory which could provide better generalization ability of the ensemble while achieving a practically acceptable level of effectiveness. The problem of information fusion is very relevant for data processing in multi-channel C-OTDR-monitoring systems. In this case we have to effectively classify targeted events which appear in the vicinity of the monitored object. Solution of this problem is based on usage of an ensemble of Lipschitz classifiers each of which corresponds to a respective channel. We suggest a brand new method for information fusion in case of ensemble of Lipschitz classifiers. This method is called “The Weighing of Inversely as Lipschitz Constants” (WILC). Results of WILC-method practical usage in multichannel C-OTDR monitoring systems are presented.

Heuristics legislation in the field of classified information as a function of training subjects of defense

OpenAIRE

БЕРЕШ ПАУН Й.

2014-01-01

Education on the protection of classified information should be the top priority when it comes to ensuring the protection of the vital interests of the state. Some information should not be made available to the public because it is mainly related to national security, and no one should question the need to protect this kind of data. This paper is intended for educators dealing with the protection of classified information, and especially to those who work with or come into contact with confi...

10 CFR 95.35 - Access to matter classified as National Security Information and Restricted Data.

Science.gov (United States)

2010-01-01

... SECURITY CLEARANCE AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION AND RESTRICTED DATA Control of Information § 95.35 Access to matter classified as National Security Information and Restricted Data. (a... have access to matter revealing Secret or Confidential National Security Information or Restricted Data...

FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

Directory of Open Access Journals (Sweden)

Ratri Enggar Pawening

2016-06-01

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

Fisher information metrics for binary classifier evaluation and training

CERN Multimedia

CERN. Geneva

2018-01-01

Different evaluation metrics for binary classifiers are appropriate to different scientific domains and even to different problems within the same domain. This presentation focuses on the optimisation of event selection to minimise statistical errors in HEP parameter estimation, a problem that is best analysed in terms of the maximisation of Fisher information about the measured parameters. After describing a general formalism to derive evaluation metrics based on Fisher information, three more specific metrics are introduced for the measurements of signal cross sections in counting experiments (FIP1) or distribution fits (FIP2) and for the measurements of other parameters from distribution fits (FIP3). The FIP2 metric is particularly interesting because it can be derived from any ROC curve, provided that prevalence is also known. In addition to its relation to measurement errors when used as an evaluation criterion (which makes it more interesting that the ROC AUC), a further advantage of the FIP2 metric is ...

An Informed Framework for Training Classifiers from Social Media

Directory of Open Access Journals (Sweden)

Dong Seon Cheng

2016-04-01

Full Text Available Extracting information from social media has become a major focus of companies and researchers in recent years. Aside from the study of the social aspects, it has also been found feasible to exploit the collaborative strength of crowds to help solve classical machine learning problems like object recognition. In this work, we focus on the generally underappreciated problem of building effective datasets for training classifiers by automatically assembling data from social media. We detail some of the challenges of this approach and outline a framework that uses expanded search queries to retrieve more qualified data. In particular, we concentrate on collaboratively tagged media on the social platform Flickr, and on the problem of image classification to evaluate our approach. Finally, we describe a novel entropy-based method to incorporate an information-theoretic principle to guide our framework. Experimental validation against well-known public datasets shows the viability of this approach and marks an improvement over the state of the art in terms of simplicity and performance.

48 CFR 53.204-1 - Safeguarding classified information within industry (DD Form 254, DD Form 441).

Science.gov (United States)

2010-10-01

... information within industry (DD Form 254, DD Form 441). 53.204-1 Section 53.204-1 Federal Acquisition....204-1 Safeguarding classified information within industry (DD Form 254, DD Form 441). The following... specified in subpart 4.4 and the clause at 52.204-2: (a) DD Form 254 (Department of Defense (DOD)), Contract...

The Motivation of Betrayal by Leaking of Classified Information

Directory of Open Access Journals (Sweden)

Lăzăroiu Laurențiu-Leonard

2017-03-01

Full Text Available Trying to forecast the human behavior involves acts and knowledge of motivational theories, applicable to profile of each organization and in particular to each individual’s style. The anticipation of personal attitudes has not the only aim for a passive monitoring of professional activity, but also wants to increase performance of risk avoidance, in acordance with a specific organizational environment. The emergence and development of motivational forms and values, whose projections determine social crimes, are risk factors, affecting the professional activity of the person, but also affecting the performance and stability of the institution. Moreover, if the motivation determines attitudes aimed at compromising classified information, the resulting actions may be considered as threats to national security. The prevention of such threats can only be achieved by understanding motivational mechanisms and external conditions for the perssonel that make it possible to transform some intentions into real actions.

78 FR 5828 - Agency Information Collection Activities: Petition To Classify Orphan as an Immediate Relative...

Science.gov (United States)

2013-01-28

...-0028] Agency Information Collection Activities: Petition To Classify Orphan as an Immediate Relative, Form I-600; Application for Advance Processing of Orphan Petition, Form I-600A; Listing of Adult Member... are encouraged and will be accepted until February 27, 2013. This process is conducted in accordance...

Klasifikasi Teks Bahasa Bali dengan Metode Information Gain dan Naive Bayes Classifier

Directory of Open Access Journals (Sweden)

Ida Bagus Gede Widnyana Putra

2016-11-01

Full Text Available Ketersediaan dokumen teks bahasa Bali yang meningkat jumlahnya membuat proses pencarian informasi pada dokumen teks berbahasa Bali menjadi semakin sulit. Mengklasifikasikanya secara manual menjadi tidak efisien mengingat peningkatan jumlah dokumen yang semakin banyak. Pada penelitian ini dikembangkan sebuah aplikasi yang dapat mengklasifikasikan teks bahasa Bali ke dalam kategori yang ditentukan. Aplikasi ini menggunakan metode klasifikasi Naive Bayes Classifier (NBC dan metode Information Gain(IG untuk seleksi fitur. Aplikasi ini diuji dengan teknik cross validation. Hasilnya adalah nilai rata-rata akurasi dari 10 fold cross validation sebesar  95,22%.

Just-in-time adaptive classifiers-part II: designing the classifier.

Science.gov (United States)

Alippi, Cesare; Roveri, Manuel

2008-12-01

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

OpenAIRE

Wei-Jong Yang; Wei-Hau Du; Pau-Choo Chang; Jar-Ferr Yang; Pi-Hsia Hung

2017-01-01

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an importan...

LCC: Light Curves Classifier

Science.gov (United States)

Vo, Martin

2017-08-01

Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

Verification of classified fissile material using unclassified attributes

International Nuclear Information System (INIS)

Nicholas, N.J.; Fearey, B.L.; Puckett, J.M.; Tape, J.W.

1998-01-01

This paper reports on the most recent efforts of US technical experts to explore verification by IAEA of unclassified attributes of classified excess fissile material. Two propositions are discussed: (1) that multiple unclassified attributes could be declared by the host nation and then verified (and reverified) by the IAEA in order to provide confidence in that declaration of a classified (or unclassified) inventory while protecting classified or sensitive information; and (2) that attributes could be measured, remeasured, or monitored to provide continuity of knowledge in a nonintrusive and unclassified manner. They believe attributes should relate to characteristics of excess weapons materials and should be verifiable and authenticatable with methods usable by IAEA inspectors. Further, attributes (along with the methods to measure them) must not reveal any classified information. The approach that the authors have taken is as follows: (1) assume certain attributes of classified excess material, (2) identify passive signatures, (3) determine range of applicable measurement physics, (4) develop a set of criteria to assess and select measurement technologies, (5) select existing instrumentation for proof-of-principle measurements and demonstration, and (6) develop and design information barriers to protect classified information. While the attribute verification concepts and measurements discussed in this paper appear promising, neither the attribute verification approach nor the measurement technologies have been fully developed, tested, and evaluated

Identification of Classified Information in Unclassified DoD Systems During the Audit of Internal Controls and Data Reliability in the Deployable Disbursing System

Science.gov (United States)

2009-02-17

Identification of Classified Information in Unclassified DoD Systems During the Audit of Internal Controls and Data Reliability in the Deployable...TITLE AND SUBTITLE Identification of Classified Information in Unclassified DoD Systems During the Audit of Internal Controls and Data Reliability...Systems During the Audit ofInternal Controls and Data Reliability in the Deployable Disbursing System (Report No. D-2009-054) Weare providing this

SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system

Directory of Open Access Journals (Sweden)

Julià-Sapé Margarida

2010-02-01

Full Text Available Abstract Background SpectraClassifier (SC is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward, and feature extraction (PCA. Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC curves. Results SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel and high resolution tissue MRS (HRMAS, processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin. In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used. Conclusions SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools.

IAEA safeguards and classified materials

International Nuclear Information System (INIS)

Pilat, J.F.; Eccleston, G.W.; Fearey, B.L.; Nicholas, N.J.; Tape, J.W.; Kratzer, M.

1997-01-01

The international community in the post-Cold War period has suggested that the International Atomic Energy Agency (IAEA) utilize its expertise in support of the arms control and disarmament process in unprecedented ways. The pledges of the US and Russian presidents to place excess defense materials, some of which are classified, under some type of international inspections raises the prospect of using IAEA safeguards approaches for monitoring classified materials. A traditional safeguards approach, based on nuclear material accountancy, would seem unavoidably to reveal classified information. However, further analysis of the IAEA's safeguards approaches is warranted in order to understand fully the scope and nature of any problems. The issues are complex and difficult, and it is expected that common technical understandings will be essential for their resolution. Accordingly, this paper examines and compares traditional safeguards item accounting of fuel at a nuclear power station (especially spent fuel) with the challenges presented by inspections of classified materials. This analysis is intended to delineate more clearly the problems as well as reveal possible approaches, techniques, and technologies that could allow the adaptation of safeguards to the unprecedented task of inspecting classified materials. It is also hoped that a discussion of these issues can advance ongoing political-technical debates on international inspections of excess classified materials

3 CFR - Implementation of the Executive Order, “Classified National Security Information”

Science.gov (United States)

2010-01-01

... 29, 2009 Implementation of the Executive Order, “Classified National Security Information” Memorandum..., “Classified National Security Information” (the “order”), which substantially advances my goals for reforming... or handles classified information shall provide the Director of the Information Security Oversight...

Just-in-time classifiers for recurrent concepts.

Science.gov (United States)

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

2013-04-01

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.

A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

Directory of Open Access Journals (Sweden)

JORGE FLORES CRUZ

2017-01-01

Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

41 CFR 105-62.102 - Authority to originally classify.

Science.gov (United States)

2010-07-01

... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable...

Neural network classifier of attacks in IP telephony

Science.gov (United States)

Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

2014-05-01

Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

Building an automated SOAP classifier for emergency department reports.

Science.gov (United States)

Mowery, Danielle; Wiebe, Janyce; Visweswaran, Shyam; Harkema, Henk; Chapman, Wendy W

2012-02-01

Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined. The first step to evaluating the SOAP framework's usefulness for clinical information extraction is to apply the model to clinical narratives and develop an automated SOAP classifier that classifies sentences from clinical reports. In this quantitative study, we applied the SOAP framework to sentences from emergency department reports, and trained and evaluated SOAP classifiers built with various linguistic features. We found the SOAP framework can be applied manually to emergency department reports with high agreement (Cohen's kappa coefficients over 0.70). Using a variety of features, we found classifiers for each SOAP class can be created with moderate to outstanding performance with F(1) scores of 93.9 (subjective), 94.5 (objective), 75.7 (assessment), and 77.0 (plan). We look forward to expanding the framework and applying the SOAP classification to clinical information extraction tasks. Copyright © 2011. Published by Elsevier Inc.

To Classify Spontaneous Motion Intention of Step Size by Using Cerebral Hemoglobin Information

Directory of Open Access Journals (Sweden)

Zhu Kai

2017-01-01

Full Text Available To improve the effect of walking-assistive devices, there is a need for it to develop devices controlled by spontaneous intention of patients. In recent study, we identified spontaneous motion intention of walking step based on cerebral hemoglobin information. Twenty healthy subjects performed walking tasks in three levels of step size (small, normal and large. According to distribution features of signals’ power spectral-density, six frequency bands (0-0.18Hz with an interval of 0.03Hz for each band width divided by applying wavelet packets decomposition were mainly analyzed. Feature vectors were extracted from the difference between oxygenated hemoglobin (oxyHb and deoxygenated hemoglobin (dexoyHb in different measuring channels in the six frequency bands. Support vector machine (SVM method was utilized to classify the three levels of step sizes. Mean recognition accuracy achieved up to 83.3%. The result indicated that it is possible to identify spontaneous walking by using cerebral hemoglobin information. This is helpful for enhancing the intelligence of walking-assistive devices and motivating the active control of patients, which further is profitable for enhancing self-confidence of patients.

Ship localization in Santa Barbara Channel using machine learning classifiers.

Science.gov (United States)

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

Classifier fusion for VoIP attacks classification

Science.gov (United States)

Safarik, Jakub; Rezac, Filip

2017-05-01

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

Classifying Microorganisms

DEFF Research Database (Denmark)

Sommerlund, Julie

2006-01-01

This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

Machine learning classifiers and fMRI: a tutorial overview.

Science.gov (United States)

Pereira, Francisco; Mitchell, Tom; Botvinick, Matthew

2009-03-01

Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).

Data Stream Classification Based on the Gamma Classifier

Directory of Open Access Journals (Sweden)

Abril Valeria Uriarte-Arcia

2015-01-01

Full Text Available The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Local-global classifier fusion for screening chest radiographs

Science.gov (United States)

Ding, Meng; Antani, Sameer; Jaeger, Stefan; Xue, Zhiyun; Candemir, Sema; Kohli, Marc; Thoma, George

2017-03-01

Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is critical for population screening, especially in medical resource constrained developing regions. In this article, we describe steps that improve previously reported performance of NLM's CXR screening algorithms and help advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary classification systems are combined. The local classifier focuses on subtle and partial presentation of the disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition, the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve, sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global fusion method over any single classifier.

A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

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WANG Shaoyu

2016-12-01

Full Text Available Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.

36 CFR 1260.20 - Who is responsible for the declassification of classified national security Executive Branch...

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2010-07-01

... declassification of classified national security Executive Branch information that has been accessioned by NARA... ADMINISTRATION DECLASSIFICATION DECLASSIFICATION OF NATIONAL SECURITY INFORMATION Responsibilities § 1260.20 Who is responsible for the declassification of classified national security Executive Branch information...

Recognition of pornographic web pages by classifying texts and images.

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Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

2007-06-01

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Fuzzy prototype classifier based on items and its application in recommender system

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Mei Cai

2017-01-01

Full Text Available Currently, recommender systems (RS are incorporating implicit information from social circle of the Internet. The implicit social information in human mind is not easy to reflect in appropriate decision making techniques. This paper consists of 2 contributions. First, we develop an item-based prototype classifier (IPC in which a prototype represents a social circlers preferences as a pattern classification technique. We assume the social circle which distinguishes with others by the items their members like. The prototype structure of the classifier is defined by two2-dimensional matrices. We use information gain and OWA aggregator to construct a feature space. The item-based classifier assigns a new item to some prototypes with different prototypicalities. We reform a typical data setmIris data set in UCI Machine Learning Repository to verify our fuzzy prototype classifier. The second proposition of this paper is to give the application of IPC in recommender system to solve new item cold-start problems. We modify the dataset of MovieLens to perform experimental demonstrations of the proposed ideas.

Deconstructing Cross-Entropy for Probabilistic Binary Classifiers

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Daniel Ramos

2018-03-01

Full Text Available In this work, we analyze the cross-entropy function, widely used in classifiers both as a performance measure and as an optimization objective. We contextualize cross-entropy in the light of Bayesian decision theory, the formal probabilistic framework for making decisions, and we thoroughly analyze its motivation, meaning and interpretation from an information-theoretical point of view. In this sense, this article presents several contributions: First, we explicitly analyze the contribution to cross-entropy of (i prior knowledge; and (ii the value of the features in the form of a likelihood ratio. Second, we introduce a decomposition of cross-entropy into two components: discrimination and calibration. This decomposition enables the measurement of different performance aspects of a classifier in a more precise way; and justifies previously reported strategies to obtain reliable probabilities by means of the calibration of the output of a discriminating classifier. Third, we give different information-theoretical interpretations of cross-entropy, which can be useful in different application scenarios, and which are related to the concept of reference probabilities. Fourth, we present an analysis tool, the Empirical Cross-Entropy (ECE plot, a compact representation of cross-entropy and its aforementioned decomposition. We show the power of ECE plots, as compared to other classical performance representations, in two diverse experimental examples: a speaker verification system, and a forensic case where some glass findings are present.

48 CFR 952.223-76 - Conditional payment of fee or profit-safeguarding restricted data and other classified...

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2010-10-01

... disclosure of Top Secret Restricted Data or other information classified as Top Secret, any classification... result in the loss, compromise, or unauthorized disclosure of Top Secret Restricted Data, or other information classified as Top Secret, any classification level of information in a SAP, information identified...

An ensemble self-training protein interaction article classifier.

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Chen, Yifei; Hou, Ping; Manderick, Bernard

2014-01-01

Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

Discrimination-Aware Classifiers for Student Performance Prediction

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Luo, Ling; Koprinska, Irena; Liu, Wei

2015-01-01

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but…

Hybrid classifiers methods of data, knowledge, and classifier combination

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Wozniak, Michal

2014-01-01

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

A distributed approach for optimizing cascaded classifier topologies in real-time stream mining systems.

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Foo, Brian; van der Schaar, Mihaela

2010-11-01

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

36 CFR 1260.22 - Who is responsible for the declassification of classified national security White House...

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2010-07-01

... declassification of classified national security White House originated information in NARA's holdings? 1260.22... for the declassification of classified national security White House originated information in NARA's... was originated by: (1) The President; (2) The White House staff; (3) Committees, commissions, or...

Classifying objects in LWIR imagery via CNNs

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Rodger, Iain; Connor, Barry; Robertson, Neil M.

2016-10-01

The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

Disassembly and Sanitization of Classified Matter

International Nuclear Information System (INIS)

Stockham, Dwight J.; Saad, Max P.

2008-01-01

The Disassembly Sanitization Operation (DSO) process was implemented to support weapon disassembly and disposition by using recycling and waste minimization measures. This process was initiated by treaty agreements and reconfigurations within both the DOD and DOE Complexes. The DOE is faced with disassembling and disposing of a huge inventory of retired weapons, components, training equipment, spare parts, weapon maintenance equipment, and associated material. In addition, regulations have caused a dramatic increase in the need for information required to support the handling and disposition of these parts and materials. In the past, huge inventories of classified weapon components were required to have long-term storage at Sandia and at many other locations throughout the DoE Complex. These materials are placed in onsite storage unit due to classification issues and they may also contain radiological and/or hazardous components. Since no disposal options exist for this material, the only choice was long-term storage. Long-term storage is costly and somewhat problematic, requiring a secured storage area, monitoring, auditing, and presenting the potential for loss or theft of the material. Overall recycling rates for materials sent through the DSO process have enabled 70 to 80% of these components to be recycled. These components are made of high quality materials and once this material has been sanitized, the demand for the component metals for recycling efforts is very high. The DSO process for NGPF, classified components established the credibility of this technique for addressing the long-term storage requirements of the classified weapons component inventory. The success of this application has generated interest from other Sandia organizations and other locations throughout the complex. Other organizations are requesting the help of the DSO team and the DSO is responding to these requests by expanding its scope to include Work-for- Other projects. For example

Maximum margin classifier working in a set of strings.

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Koyano, Hitoshi; Hayashida, Morihiro; Akutsu, Tatsuya

2016-03-01

Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem for a consensus sequence of strings demonstrated by one of the authors and co-workers in a previous study. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein-protein interactions using amino acid sequences and classifying RNAs by the secondary structure using nucleotide sequences.

Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

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Asamaporn Sitthi

2016-09-01

Full Text Available Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery containing an immense amount of information. In this paper, geotagged Flickr images are used for automatic extraction of low-level land use/land cover (LULC features. The proposed method uses a naive Bayes classifier with color, shape, and color index descriptors. The classified images are mapped using a majority filtering approach. The classifier performance in overall accuracy, kappa coefficient, precision, recall, and f-measure was 87.94%, 82.89%, 88.20%, 87.90%, and 88%, respectively. Labeled-crowdsourced images were filtered into a spatial tile of a 30 m × 30 m resolution using the majority voting method to reduce geolocation uncertainty from the crowdsourced data. These tile datasets were used as training and validation samples to classify Landsat TM5 images. The supervised maximum likelihood method was used for the LULC classification. The results show that the geotagged Flickr images can classify LULC types with reasonable accuracy and that the proposed approach improves LULC classification efficiency if a sufficient spatial distribution of crowdsourced data exists.

Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method

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S. Sivakumari

2009-03-01

Full Text Available This paper intends to classify the Ljubljana Breast Cancer dataset using C4.5 Decision Tree and Nai?ve Bayes classifiers. In this work, classification is carriedout using two methods. In the first method, dataset is analysed using all the attributes in the dataset. In the second method, attributes are ranked using information gain ranking technique and only the high ranked attributes are used to build the classification model. We are evaluating the results of C4.5 Decision Tree and Nai?ve Bayes classifiers in terms of classifier accuracy for various folds of cross validation. Our results show that both the classifiers achieve good accuracy on the dataset.

Information barriers and authentication

International Nuclear Information System (INIS)

MacArthur, D.W.; Wolford, J.K.

2001-01-01

Acceptance of nuclear materials into a monitoring regime is complicated if the materials are in classified shapes or have classified composition. An attribute measurement system with an information barrier can be emplo,yed to generate an unclassified display from classified measurements. This information barrier must meet two criteria: (1) classified information cannot be released to the monitoring party, and (2) the monitoring party must be convinced that the unclassified output accurately represents the classified input. Criterion 1 is critical to the host country to protect the classified information. Criterion 2 is critical to the monitoring party and is often termed the 'authentication problem.' Thus, the necessity for authentication of a measurement system with an information barrier stems directly from the description of a useful information barrier. Authentication issues must be continually addressed during the entire development lifecycle of the measurement system as opposed to being applied only after the system is built.

Quantum ensembles of quantum classifiers.

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Schuld, Maria; Petruccione, Francesco

2018-02-09

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

Stack filter classifiers

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Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

2009-01-01

Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

Classifying a smoker scale in adult daily and nondaily smokers.

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Pulvers, Kim; Scheuermann, Taneisha S; Romero, Devan R; Basora, Brittany; Luo, Xianghua; Ahluwalia, Jasjit S

2014-05-01

Smoker identity, or the strength of beliefs about oneself as a smoker, is a robust marker of smoking behavior. However, many nondaily smokers do not identify as smokers, underestimating their risk for tobacco-related disease and resulting in missed intervention opportunities. Assessing underlying beliefs about characteristics used to classify smokers may help explain the discrepancy between smoking behavior and smoker identity. This study examines the factor structure, reliability, and validity of the Classifying a Smoker scale among a racially diverse sample of adult smokers. A cross-sectional survey was administered through an online panel survey service to 2,376 current smokers who were at least 25 years of age. The sample was stratified to obtain equal numbers of 3 racial/ethnic groups (African American, Latino, and White) across smoking level (nondaily and daily smoking). The Classifying a Smoker scale displayed a single factor structure and excellent internal consistency (α = .91). Classifying a Smoker scores significantly increased at each level of smoking, F(3,2375) = 23.68, p smoker identity, stronger dependence on cigarettes, greater health risk perceptions, more smoking friends, and were more likely to carry cigarettes. Classifying a Smoker scores explained unique variance in smoking variables above and beyond that explained by smoker identity. The present study supports the use of the Classifying a Smoker scale among diverse, experienced smokers. Stronger endorsement of characteristics used to classify a smoker (i.e., stricter criteria) was positively associated with heavier smoking and related characteristics. Prospective studies are needed to inform prevention and treatment efforts.

Comparison of artificial intelligence classifiers for SIP attack data

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Safarik, Jakub; Slachta, Jiri

2016-05-01

Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

Trilateral Initiative: IAEA authentication and national certification of verification equipment for facilities with classified forms of fissile material

International Nuclear Information System (INIS)

Haas, Eckard; Sukhanov, Alexander; Murphy, John

2001-01-01

Full text: Within the framework of the Trilateral Initiative, technical challenges have arisen due to the potential of the International Atomic Energy Agency (IAEA) monitoring fissile material with classified characteristics, as well as the IAEA using facility or host country supplied monitoring equipment. In monitoring material with classified characteristics, it is recognized that the host country needs to assure that classified information is not made available to the IAEA inspectors. Thus, any monitoring equipment used to monitor material with classified characteristics has to contain information security capabilities, such as information barriers. But likewise in using host-country-supplied monitoring equipment, regarding the material being monitored the IAEA has to have confidence that the information provided by the equipment is genuine and can be used by the IAEA in fulfilling its obligation to derive conclusions based on independent verification measures. Thus the IAEA needs to go through the process of authenticating the monitoring equipment. In the same way the host country needs to go through the process to assure itself that the monitoring equipment integrated with an information barrier will not divulge any classified information about an inspected sensitive item. Both processes require on large extent identical measures, but partially also may conflict with each other. The fact that monitoring equipment needs to exhibit information security throughout its lifecycle while at the same time be capable of being authenticated necessitates the need for creative technical approaches to be pursued. (author)

Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

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Tsatsoulis Costas

2010-05-01

Full Text Available Abstract Background There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives us insight into a gene's functionality by informing us how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, we analyzed if we could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results We performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. Our results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the percentage of correctly classified genes to their correct Gene Ontology Slim term for each ontology reaches over 80% with high accuracy (reflected in F-measures over 0.80 of the classification rules produced. Conclusions We confirmed that in classifying genes to their correct Gene Ontology Slim term, the inclusion of neighbour information from those genes is beneficial. Knowing the location of a gene and the Gene Ontology Slim information from neighbouring genes gives us insight into that gene's functionality. This benefit is seen by just including information from a gene's two-nearest neighbouring genes.

Parameterization of a fuzzy classifier for the diagnosis of an industrial process

International Nuclear Information System (INIS)

Toscano, R.; Lyonnet, P.

2002-01-01

The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant

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

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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

Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

International Nuclear Information System (INIS)

Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

2011-01-01

The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

Statistical text classifier to detect specific type of medical incidents.

Science.gov (United States)

Wong, Zoie Shui-Yee; Akiyama, Masanori

2013-01-01

WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

Science.gov (United States)

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

1998-01-01

Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

Classifier for gravitational-wave inspiral signals in nonideal single-detector data

Science.gov (United States)

Kapadia, S. J.; Dent, T.; Dal Canton, T.

2017-11-01

We describe a multivariate classifier for candidate events in a templated search for gravitational-wave (GW) inspiral signals from neutron-star-black-hole (NS-BH) binaries, in data from ground-based detectors where sensitivity is limited by non-Gaussian noise transients. The standard signal-to-noise ratio (SNR) and chi-squared test for inspiral searches use only properties of a single matched filter at the time of an event; instead, we propose a classifier using features derived from a bank of inspiral templates around the time of each event, and also from a search using approximate sine-Gaussian templates. The classifier thus extracts additional information from strain data to discriminate inspiral signals from noise transients. We evaluate a random forest classifier on a set of single-detector events obtained from realistic simulated advanced LIGO data, using simulated NS-BH signals added to the data. The new classifier detects a factor of 1.5-2 more signals at low false positive rates as compared to the standard "reweighted SNR" statistic, and does not require the chi-squared test to be computed. Conversely, if only the SNR and chi-squared values of single-detector events are available, random forest classification performs nearly identically to the reweighted SNR.

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

Science.gov (United States)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

Science.gov (United States)

Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2017-02-27

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Directory of Open Access Journals (Sweden)

Simon Fong

2017-02-01

Full Text Available In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

32 CFR 154.6 - Standards for access to classified information or assignment to sensitive duties.

Science.gov (United States)

2010-07-01

... OF THE SECRETARY OF DEFENSE SECURITY DEPARTMENT OF DEFENSE PERSONNEL SECURITY PROGRAM REGULATION... person's loyalty, reliability, and trustworthiness are such that entrusting the person with classified... reasonable basis for doubting the person's loyalty to the Government of the United States. ...

Fingerprint prediction using classifier ensembles

CSIR Research Space (South Africa)

Molale, P

2011-11-01

Full Text Available ); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems...

On-line computing in a classified environment

International Nuclear Information System (INIS)

O'Callaghan, P.B.

1982-01-01

Westinghouse Hanford Company (WHC) recently developed a Department of Energy (DOE) approved real-time, on-line computer system to control nuclear material. The system simultaneously processes both classified and unclassified information. Implementation of this system required application of many security techniques. The system has a secure, but user friendly interface. Many software applications protect the integrity of the data base from malevolent or accidental errors. Programming practices ensure the integrity of the computer system software. The audit trail and the reports generation capability record user actions and status of the nuclear material inventory

Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.

Science.gov (United States)

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; McDonald, Elizabeth S; Rosen, Mark; Mies, Carolyn; Feldman, Michael; Kontos, Despina

2015-06-01

Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer. Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay. Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94). The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information. HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.

Classifying Sluice Occurrences in Dialogue

DEFF Research Database (Denmark)

Baird, Austin; Hamza, Anissa; Hardt, Daniel

2018-01-01

perform manual annotation with acceptable inter-coder agreement. We build classifier models with Decision Trees and Naive Bayes, with accuracy of 67%. We deploy a classifier to automatically classify sluice occurrences in OpenSubtitles, resulting in a corpus with 1.7 million occurrences. This will support....... Despite this, the corpus can be of great use in research on sluicing and development of systems, and we are making the corpus freely available on request. Furthermore, we are in the process of improving the accuracy of sluice identification and annotation for the purpose of created a subsequent version...

An informal summary of a new formalism for classifying spin-orbit systems using tools distilled from the theory of bundles

International Nuclear Information System (INIS)

Heinemann, K.; Ellison, J.A.

2015-02-01

We give an informal summary of ongoing work which uses tools distilled from the theory of fibre bundles to classify and connect invariant fields associated with spin motion in storage rings. We mention four major theorems. One ties invariant fields with the notion of normal form, the second allows comparison of different invariant fields and the two others tie the existence of invariant fields to the existence of certain invariant sets. We explain how the theorems apply to the spin dynamics of spin-1/2 and spin-1 particles. Our approach elegantly unifies the spin-vector dynamics from the T-BMT equation with the spin-tensor dynamics and other dynamics and suggests an avenue for addressing the question of the existence of the invariant spin field.

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha

2013-11-25

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Information Gain Based Dimensionality Selection for Classifying Text Documents

Energy Technology Data Exchange (ETDEWEB)

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexity is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.

Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

Science.gov (United States)

Khoury, Mehdi; Liu, Honghai

This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

Classifier models and architectures for EEG-based neonatal seizure detection

International Nuclear Information System (INIS)

Greene, B R; Marnane, W P; Lightbody, G; Reilly, R B; Boylan, G B

2008-01-01

Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multi-channel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

Directory of Open Access Journals (Sweden)

Shehzad Khalid

2014-01-01

Full Text Available We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

Predict or classify: The deceptive role of time-locking in brain signal classification

Science.gov (United States)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier

Directory of Open Access Journals (Sweden)

Hong Wang

2015-07-01

Full Text Available The textural and spatial information extracted from very high resolution (VHR remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM textures and a local statistical analysis Getis statistic (Gi, computed from IKONOS multispectral (MS imagery acquired from the Yellow River Delta in China, along with a random forest (RF classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation were first calculated from IKONOS NIR band (Band 4 to determine an optimal window size (13 × 13 and an optimal direction (45°. Then, the optimal window size and direction were applied to the three other IKONOS MS bands (blue, green, and red for calculating the eight GLCM textures. Next, an optimal distance value (5 and an optimal neighborhood rule (Queen’s case were determined for calculating the four Gi features from the four IKONOS MS bands. Finally, different RF classification results of the three forest health conditions were created: (1 an overall accuracy (OA of 79.5% produced using the four MS band reflectances only; (2 an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3 an OA of 93.3% created with the all 32 GLCM features calculated from the four IKONOS MS bands with a window size of 13 × 13 and direction of 45°; (4 an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (5 an OA of 96.9% created with the combined 16 spectral (four, spatial (four, and textural (eight features. The most important feature ranked by RF

Error minimizing algorithms for nearest eighbor classifiers

Energy Technology Data Exchange (ETDEWEB)

Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M

2011-01-03

Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

Aggregation Operator Based Fuzzy Pattern Classifier Design

DEFF Research Database (Denmark)

Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

2009-01-01

This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

Composite Classifiers for Automatic Target Recognition

National Research Council Canada - National Science Library

Wang, Lin-Cheng

1998-01-01

...) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers...

Classifying and Designing the Educational Methods with Information Communications Technoligies

Directory of Open Access Journals (Sweden)

I. N. Semenova

2013-01-01

Full Text Available The article describes the conceptual apparatus for implementing the Information Communications Technologies (ICT in education. The authors suggest the classification variants of the related teaching methods according to the following component combinations: types of students work with information, goals of ICT incorporation into the training process, individualization degrees, contingent involvement, activity levels and pedagogical field targets, ideology of informational didactics, etc. Each classification can solve the educational tasks in the context of the partial paradigm of modern didactics; any kind of methods implies the particular combination of activities in educational environment.The whole spectrum of classifications provides the informational functional basis for the adequate selection of necessary teaching methods in accordance with the specified goals and planned results. The potential variants of ICT implementation methods are given for different teaching models. 

Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

Directory of Open Access Journals (Sweden)

Bogdan Gliwa

2011-01-01

Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

Science.gov (United States)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Class-specific Error Bounds for Ensemble Classifiers

Energy Technology Data Exchange (ETDEWEB)

Prenger, R; Lemmond, T; Varshney, K; Chen, B; Hanley, W

2009-10-06

The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missed detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.

Deconvolution When Classifying Noisy Data Involving Transformations

KAUST Repository

Carroll, Raymond

2012-09-01

In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

Deconvolution When Classifying Noisy Data Involving Transformations.

Science.gov (United States)

Carroll, Raymond; Delaigle, Aurore; Hall, Peter

2012-09-01

In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

Deconvolution When Classifying Noisy Data Involving Transformations

KAUST Repository

Carroll, Raymond; Delaigle, Aurore; Hall, Peter

2012-01-01

In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

Science.gov (United States)

Xu, Wei; Peng, Yong

2015-01-01

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

A New Adaptive Structural Signature for Symbol Recognition by Using a Galois Lattice as a Classifier.

Science.gov (United States)

Coustaty, M; Bertet, K; Visani, M; Ogier, J

2011-08-01

In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois lattice as a classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures-that can be seen as dynamic paths which carry high-level information-are robust toward various transformations. They are classified by using a Galois lattice as a classifier. The performance of the proposed approach is evaluated based on the GREC'03 symbol database, and the experimental results we obtain are encouraging.

Super resolution reconstruction of infrared images based on classified dictionary learning

Science.gov (United States)

Liu, Fei; Han, Pingli; Wang, Yi; Li, Xuan; Bai, Lu; Shao, Xiaopeng

2018-05-01

Infrared images always suffer from low-resolution problems resulting from limitations of imaging devices. An economical approach to combat this problem involves reconstructing high-resolution images by reasonable methods without updating devices. Inspired by compressed sensing theory, this study presents and demonstrates a Classified Dictionary Learning method to reconstruct high-resolution infrared images. It classifies features of the samples into several reasonable clusters and trained a dictionary pair for each cluster. The optimal pair of dictionaries is chosen for each image reconstruction and therefore, more satisfactory results is achieved without the increase in computational complexity and time cost. Experiments and results demonstrated that it is a viable method for infrared images reconstruction since it improves image resolution and recovers detailed information of targets.

Security classification of information

Energy Technology Data Exchange (ETDEWEB)

Quist, A.S.

1993-04-01

This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

Comparing classifiers for pronunciation error detection

NARCIS (Netherlands)

Strik, H.; Truong, K.; Wet, F. de; Cucchiarini, C.

2007-01-01

Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs

Classifier Fusion With Contextual Reliability Evaluation.

Science.gov (United States)

Liu, Zhunga; Pan, Quan; Dezert, Jean; Han, Jun-Wei; He, You

2018-05-01

Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.

Hierarchical mixtures of naive Bayes classifiers

NARCIS (Netherlands)

Wiering, M.A.

2002-01-01

Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan

2014-09-07

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

2014-01-01

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

A Supervised Multiclass Classifier for an Autocoding System

Directory of Open Access Journals (Sweden)

Yukako Toko

2017-11-01

Full Text Available Classification is often required in various contexts, including in the field of official statistics. In the previous study, we have developed a multiclass classifier that can classify short text descriptions with high accuracy. The algorithm borrows the concept of the naïve Bayes classifier and is so simple that its structure is easily understandable. The proposed classifier has the following two advantages. First, the processing times for both learning and classifying are extremely practical. Second, the proposed classifier yields high-accuracy results for a large portion of a dataset. We have previously developed an autocoding system for the Family Income and Expenditure Survey in Japan that has a better performing classifier. While the original system was developed in Perl in order to improve the efficiency of the coding process of short Japanese texts, the proposed system is implemented in the R programming language in order to explore versatility and is modified to make the system easily applicable to English text descriptions, in consideration of the increasing number of R users in the field of official statistics. We are planning to publish the proposed classifier as an R-package. The proposed classifier would be generally applicable to other classification tasks including coding activities in the field of official statistics, and it would contribute greatly to improving their efficiency.

Is it important to classify ischaemic stroke?

LENUS (Irish Health Repository)

Iqbal, M

2012-02-01

Thirty-five percent of all ischemic events remain classified as cryptogenic. This study was conducted to ascertain the accuracy of diagnosis of ischaemic stroke based on information given in the medical notes. It was tested by applying the clinical information to the (TOAST) criteria. Hundred and five patients presented with acute stroke between Jan-Jun 2007. Data was collected on 90 patients. Male to female ratio was 39:51 with age range of 47-93 years. Sixty (67%) patients had total\\/partial anterior circulation stroke; 5 (5.6%) had a lacunar stroke and in 25 (28%) the mechanism of stroke could not be identified. Four (4.4%) patients with small vessel disease were anticoagulated; 5 (5.6%) with atrial fibrillation received antiplatelet therapy and 2 (2.2%) patients with atrial fibrillation underwent CEA. This study revealed deficiencies in the clinical assessment of patients and treatment was not tailored to the mechanism of stroke in some patients.

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

International Nuclear Information System (INIS)

Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.; Lindfors, Karen K.

2015-01-01

contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets

Logarithmic learning for generalized classifier neural network.

Science.gov (United States)

Ozyildirim, Buse Melis; Avci, Mutlu

2014-12-01

Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

Science.gov (United States)

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

DECISION TREE CLASSIFIERS FOR STAR/GALAXY SEPARATION

International Nuclear Information System (INIS)

Vasconcellos, E. C.; Ruiz, R. S. R.; De Carvalho, R. R.; Capelato, H. V.; Gal, R. R.; LaBarbera, F. L.; Frago Campos Velho, H.; Trevisan, M.

2011-01-01

We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS-DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 ≤ r ≤ 21 (85.2%) and r ≥ 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT, and Ball et al. We find that our FT classifier is comparable to or better in completeness over the full magnitude range 15 ≤ r ≤ 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (>80%) while simultaneously achieving low contamination (∼2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 ≤ r ≤ 21.

Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features

Directory of Open Access Journals (Sweden)

Kittasil Silanon

2017-01-01

Full Text Available Hand posture recognition is an essential module in applications such as human-computer interaction (HCI, games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG feature (which is applied with more focus on the information within certain region of the image rather than each single pixel and Adaptive Boost (i.e., AdaBoost learning technique to select the best weak classifier and to construct a strong classifier that consists of several weak classifiers to be cascaded in detection architecture. We collected 21 static hand posture images from 10 subjects for testing and training in Thai letters finger-spelling. The parameters for the training process have been adjusted in three experiments, false positive rates (FPR, true positive rates (TPR, and number of training stages (N, to achieve the most suitable training model for each hand posture. All cascaded classifiers are loaded into the system simultaneously to classify different hand postures. A correlation coefficient is computed to distinguish the hand postures that are similar. The system achieves approximately 78% accuracy on average on all classifier experiments.

Massively Multi-core Acceleration of a Document-Similarity Classifier to Detect Web Attacks

Energy Technology Data Exchange (ETDEWEB)

Ulmer, C; Gokhale, M; Top, P; Gallagher, B; Eliassi-Rad, T

2010-01-14

This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric to two massively multi-core hardware platforms. The TFIDF classifier is used to detect web attacks in HTTP data. In our parallel hardware approaches, we design streaming, real time classifiers by simplifying the sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. Parallel implementations on the Tilera 64-core System on Chip and the Xilinx Virtex 5-LX FPGA are presented. For the Tilera, we employ a reduced state machine to recognize dictionary terms without requiring explicit tokenization, and achieve throughput of 37MB/s at slightly reduced accuracy. For the FPGA, we have developed a set of software tools to help automate the process of converting training data to synthesizable hardware and to provide a means of trading off between accuracy and resource utilization. The Xilinx Virtex 5-LX implementation requires 0.2% of the memory used by the original algorithm. At 166MB/s (80X the software) the hardware implementation is able to achieve Gigabit network throughput at the same accuracy as the original algorithm.

Attribute measurement equipment for the verification of plutonium in classified forms for the Trilateral Initiative

International Nuclear Information System (INIS)

Langner, D.G.; Hsue, S.-T.; Macarthur, D.W.

2001-01-01

Full text: A team of technical experts from the Russian Federation, the International Atomic Energy Agency (IAEA) and the United States have been working for almost five years on the development of a tool kit of instruments that could be used to verify plutonium-bearing items that have classified characteristics in nuclear weapons states. This suite of instruments is similar in many ways to standard safeguards equipment and includes high-resolution gamma-ray spectrometers, neutron multiplicity counters, gross neutron counters and gross gamma-ray detectors. In safeguards applications, this equipment is known to be robust, and authentication methods are well understood. This equipment is very intrusive, however, and a traditional safeguards application of such equipment for verification of materials with classified characteristics would reveal classified information to the inspector, Several enabling technologies have been or are being developed to facilitate the use of these trusted, but intrusive technologies. In this paper, these technologies will be described. One of the new technologies is called an Attribute Verification System with an Information Barrier Utilizing Neutron Multiplicity Counting and High-Resolution Gamma-Ray Spectrometry' or AVNG. The radiation measurement equipment, comprising a neutron multiplicity counter and high-resolution gamma-ray spectrometer, is standard safeguards-type equipment with information security features added. The information barrier is a combination of technical and procedural methods that protect classified information while allowing the inspector to have confidence that the measurement equipment is providing authentic results. The approach is to reduce the radiation data collected by the measurement equipment to a simple 'yes/no' result regarding attributes of the plutonium-bearing item. The 'yes/no' result is unclassified by design so that it can be shared with an inspector. The attributes that the Trilateral Initiative

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

Science.gov (United States)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

ECLogger: Cross-Project Catch-Block Logging Prediction Using Ensemble of Classifiers

Directory of Open Access Journals (Sweden)

Sangeeta Lal

2017-01-01

Full Text Available Background: Software developers insert log statements in the source code to record program execution information. However, optimizing the number of log statements in the source code is challenging. Machine learning based within-project logging prediction tools, proposed in previous studies, may not be suitable for new or small software projects. For such software projects, we can use cross-project logging prediction. Aim: The aim of the study presented here is to investigate cross-project logging prediction methods and techniques. Method: The proposed method is ECLogger, which is a novel, ensemble-based, cross-project, catch-block logging prediction model. In the research We use 9 base classifiers were used and combined using ensemble techniques. The performance of ECLogger was evaluated on on three open-source Java projects: Tomcat, CloudStack and Hadoop. Results: ECLogger Bagging, ECLogger AverageVote, and ECLogger MajorityVote show a considerable improvement in the average Logged F-measure (LF on 3, 5, and 4 source -> target project pairs, respectively, compared to the baseline classifiers. ECLogger AverageVote performs best and shows improvements of 3.12% (average LF and 6.08% (average ACC – Accuracy. Conclusion: The classifier based on ensemble techniques, such as bagging, average vote, and majority vote outperforms the baseline classifier. Overall, the ECLogger AverageVote model performs best. The results show that the CloudStack project is more generalizable than the other projects.

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.

Directory of Open Access Journals (Sweden)

Sebastian Bach

Full Text Available Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package.

10 CFR 110.123 - Notice of intent to introduce classified information.

Science.gov (United States)

2010-01-01

...; (2) The highest level of classification of the information (confidential, secret or other); (3) When it is anticipated that the information would be introduced; and (4) The relevance and materiality of...

Neural Network Classifiers for Local Wind Prediction.

Science.gov (United States)

Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

2004-05-01

This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

3D Bayesian contextual classifiers

DEFF Research Database (Denmark)

Larsen, Rasmus

2000-01-01

We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

Directory of Open Access Journals (Sweden)

Sang-Hoon Hong

2015-07-01

Full Text Available The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol synthetic aperture radar (PolSAR data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering. We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.

Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

Science.gov (United States)

Geng, S.; Ren, G.; Ogihara, M.

2017-05-01

Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

Carbon classified?

DEFF Research Database (Denmark)

Lippert, Ingmar

2012-01-01

. Using an actor- network theory (ANT) framework, the aim is to investigate the actors who bring together the elements needed to classify their carbon emission sources and unpack the heterogeneous relations drawn on. Based on an ethnographic study of corporate agents of ecological modernisation over...... a period of 13 months, this paper provides an exploration of three cases of enacting classification. Drawing on ANT, we problematise the silencing of a range of possible modalities of consumption facts and point to the ontological ethics involved in such performances. In a context of global warming...

76 FR 40296 - Declassification of National Security Information

Science.gov (United States)

2011-07-08

... Declassification of National Security Information AGENCY: National Archives and Records Administration. ACTION... classified national security information in records transferred to NARA's legal custody. The rule incorporates changes resulting from issuance of Executive Order 13526, Classified National Security Information...

High dimensional classifiers in the imbalanced case

DEFF Research Database (Denmark)

Bak, Britta Anker; Jensen, Jens Ledet

We consider the binary classification problem in the imbalanced case where the number of samples from the two groups differ. The classification problem is considered in the high dimensional case where the number of variables is much larger than the number of samples, and where the imbalance leads...... to a bias in the classification. A theoretical analysis of the independence classifier reveals the origin of the bias and based on this we suggest two new classifiers that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some...

Generating prior probabilities for classifiers of brain tumours using belief networks

Directory of Open Access Journals (Sweden)

Arvanitis Theodoros N

2007-09-01

Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

Balanced sensitivity functions for tuning multi-dimensional Bayesian network classifiers

NARCIS (Netherlands)

Bolt, J.H.; van der Gaag, L.C.

Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological structure, which are tailored to classifying data instances into multiple dimensions. Like more traditional classifiers, multi-dimensional classifiers are typically learned from data and may include

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

Energy Technology Data Exchange (ETDEWEB)

Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my [Dept. of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia); Hannan, M.A., E-mail: hannan@eng.ukm.my [Dept. of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia); Basri, Hassan [Dept. of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia); Hussain, Aini; Arebey, Maher [Dept. of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangore (Malaysia)

2014-02-15

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

Feature extraction using convolutional neural network for classifying breast density in mammographic images

Science.gov (United States)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

STATISTICAL TOOLS FOR CLASSIFYING GALAXY GROUP DYNAMICS

International Nuclear Information System (INIS)

Hou, Annie; Parker, Laura C.; Harris, William E.; Wilman, David J.

2009-01-01

The dynamical state of galaxy groups at intermediate redshifts can provide information about the growth of structure in the universe. We examine three goodness-of-fit tests, the Anderson-Darling (A-D), Kolmogorov, and χ 2 tests, in order to determine which statistical tool is best able to distinguish between groups that are relaxed and those that are dynamically complex. We perform Monte Carlo simulations of these three tests and show that the χ 2 test is profoundly unreliable for groups with fewer than 30 members. Power studies of the Kolmogorov and A-D tests are conducted to test their robustness for various sample sizes. We then apply these tests to a sample of the second Canadian Network for Observational Cosmology Redshift Survey (CNOC2) galaxy groups and find that the A-D test is far more reliable and powerful at detecting real departures from an underlying Gaussian distribution than the more commonly used χ 2 and Kolmogorov tests. We use this statistic to classify a sample of the CNOC2 groups and find that 34 of 106 groups are inconsistent with an underlying Gaussian velocity distribution, and thus do not appear relaxed. In addition, we compute velocity dispersion profiles (VDPs) for all groups with more than 20 members and compare the overall features of the Gaussian and non-Gaussian groups, finding that the VDPs of the non-Gaussian groups are distinct from those classified as Gaussian.

Using Neural Networks to Classify Digitized Images of Galaxies

Science.gov (United States)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

Information barriers

International Nuclear Information System (INIS)

Fuller, J.L.; Wolford, J.

2001-01-01

Full text: An information barrier (IB) consists of procedures and technology that prevent the release of sensitive information during a joint inspection of a sensitive nuclear item, and provides confidence that the measurement system into which it has been integrated functions exactly as designed and constructed. Work in the U.S. on radiation detection system information barriers dates back at least to 1990, even though the terminology is more recent. In January 1999 the Joint DoD-DOE Information Barrier Working Group was formed in the United States to help coordinate technical efforts related to information barrier R and D. This paper presents an overview of the efforts of this group, by its Chairs, as well as recommendations for further information barrier R and D. Progress on the demonstration of monitoring systems containing IBs is also provided. From the U.S. perspective, the basic, top-level functional requirements for the information barrier portion of an integrated radiation signature-information barrier inspection system are twofold: The host must be assured that his classified information is protected from disclosure to the inspecting party; and The inspecting party must be confident that the integrated inspection system measures, processes, and presents the radiation-signature-based measurement conclusion in an accurate and reproducible manner. It is the position of the United States that in the absence of any agreement to share classified nuclear weapons design information in the conduct of an inspection regime, the requirement to protect host country classified warhead design information is paramount and admits no tradeoff versus the confidence provided to the inspecting party in the accuracy and reproducibility of the measurements. The U.S. has reached an internal consensus on several critical design elements that define a general standard for radiation signature information barrier design. These criteria have stood the test of time under intense

12 CFR 792.63 - Collection of information from individuals; information forms.

Science.gov (United States)

2010-01-01

... FREEDOM OF INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED... information concerning religion, political beliefs or activities, association memberships (other than those...

Fast Most Similar Neighbor (MSN) classifiers for Mixed Data

OpenAIRE

Hernández Rodríguez, Selene

2010-01-01

The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and its good performance. However, in large datasets applications, the exhaustive k-NN classifier becomes impractical. Therefore, many fast k-NN classifiers have been developed; most of them rely on metric properties (usually the triangle inequality) to reduce the number of prototype comparisons. Hence, the existing fast k-NN classifiers are applicable only when the comparison f...

A Handbook for Derivative Classifiers at Los Alamos National Laboratory

Energy Technology Data Exchange (ETDEWEB)

Sinkula, Barbara Jean [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-02-23

The Los Alamos Classification Office (within the SAFE-IP group) prepared this handbook as a resource for the Laboratory’s derivative classifiers (DCs). It contains information about United States Government (USG) classification policy, principles, and authorities as they relate to the LANL Classification Program in general, and to the LANL DC program specifically. At a working level, DCs review Laboratory documents and material that are subject to classification review requirements, while the Classification Office provides the training and resources for DCs to perform that vital function.

Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

Directory of Open Access Journals (Sweden)

Courage Kamusoko

2014-06-01

Full Text Available Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT, random forests (RF, and support vector machines (SVM in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

Feature extraction for dynamic integration of classifiers

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

2007-01-01

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

Classifying Returns as Extreme

DEFF Research Database (Denmark)

Christiansen, Charlotte

2014-01-01

I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...

Consistency Analysis of Nearest Subspace Classifier

OpenAIRE

Wang, Yi

2015-01-01

The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...

Energy-Efficient Neuromorphic Classifiers.

Science.gov (United States)

Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

2016-10-01

Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

Uses and misuses of Bayes' rule and Bayesian classifiers in cybersecurity

Science.gov (United States)

Bard, Gregory V.

2017-12-01

This paper will discuss the applications of Bayes' Rule and Bayesian Classifiers in Cybersecurity. While the most elementary form of Bayes' rule occurs in undergraduate coursework, there are more complicated forms as well. As an extended example, Bayesian spam filtering is explored, and is in many ways the most triumphant accomplishment of Bayesian reasoning in computer science, as nearly everyone with an email address has a spam folder. Bayesian Classifiers have also been responsible significant cybersecurity research results; yet, because they are not part of the standard curriculum, few in the mathematics or information-technology communities have seen the exact definitions, requirements, and proofs that comprise the subject. Moreover, numerous errors have been made by researchers (described in this paper), due to some mathematical misunderstandings dealing with conditional independence, or other badly chosen assumptions. Finally, to provide instructors and researchers with real-world examples, 25 published cybersecurity papers that use Bayesian reasoning are given, with 2-4 sentence summaries of the focus and contributions of each paper.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

Science.gov (United States)

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

How to Name and Classify Your Phage: An Informal Guide

Directory of Open Access Journals (Sweden)

Evelien Adriaenssens

2017-04-01

Full Text Available With this informal guide, we try to assist both new and experienced phage researchers through two important stages that follow phage discovery; that is, naming and classification. Providing an appropriate name for a bacteriophage is not as trivial as it sounds, and the effects might be long-lasting in databases and in official taxon names. Phage classification is the responsibility of the Bacterial and Archaeal Viruses Subcommittee (BAVS of the International Committee on the Taxonomy of Viruses (ICTV. While the BAVS aims at providing a holistic approach to phage taxonomy, for individual researchers who have isolated and sequenced a new phage, this can be a little overwhelming. We are now providing these researchers with an informal guide to phage naming and classification, taking a “bottom-up” approach from the phage isolate level.

Immunohistochemical analysis of breast tissue microarray images using contextual classifiers

Directory of Open Access Journals (Sweden)

Stephen J McKenna

2013-01-01

Full Text Available Background: Tissue microarrays (TMAs are an important tool in translational research for examining multiple cancers for molecular and protein markers. Automatic immunohistochemical (IHC scoring of breast TMA images remains a challenging problem. Methods: A two-stage approach that involves localization of regions of invasive and in-situ carcinoma followed by ordinal IHC scoring of nuclei in these regions is proposed. The localization stage classifies locations on a grid as tumor or non-tumor based on local image features. These classifications are then refined using an auto-context algorithm called spin-context. Spin-context uses a series of classifiers to integrate image feature information with spatial context information in the form of estimated class probabilities. This is achieved in a rotationally-invariant manner. The second stage estimates ordinal IHC scores in terms of the strength of staining and the proportion of nuclei stained. These estimates take the form of posterior probabilities, enabling images with uncertain scores to be referred for pathologist review. Results: The method was validated against manual pathologist scoring on two nuclear markers, progesterone receptor (PR and estrogen receptor (ER. Errors for PR data were consistently lower than those achieved with ER data. Scoring was in terms of estimated proportion of cells that were positively stained (scored on an ordinal scale of 0-6 and perceived strength of staining (scored on an ordinal scale of 0-3. Average absolute differences between predicted scores and pathologist-assigned scores were 0.74 for proportion of cells and 0.35 for strength of staining (PR. Conclusions: The use of context information via spin-context improved the precision and recall of tumor localization. The combination of the spin-context localization method with the automated scoring method resulted in reduced IHC scoring errors.

Reinforcement Learning Based Artificial Immune Classifier

Directory of Open Access Journals (Sweden)

Mehmet Karakose

2013-01-01

Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

Energy Technology Data Exchange (ETDEWEB)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2014-05-15

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

International Nuclear Information System (INIS)

Jing, Yaqi; Meng, Qinghao; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen

2014-01-01

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively

Intelligent Garbage Classifier

Directory of Open Access Journals (Sweden)

Ignacio Rodríguez Novelle

2008-12-01

Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

Information-preserving structures: A general framework for quantum zero-error information

International Nuclear Information System (INIS)

Blume-Kohout, Robin; Ng, Hui Khoon; Poulin, David; Viola, Lorenza

2010-01-01

Quantum systems carry information. Quantum theory supports at least two distinct kinds of information (classical and quantum), and a variety of different ways to encode and preserve information in physical systems. A system's ability to carry information is constrained and defined by the noise in its dynamics. This paper introduces an operational framework, using information-preserving structures, to classify all the kinds of information that can be perfectly (i.e., with zero error) preserved by quantum dynamics. We prove that every perfectly preserved code has the same structure as a matrix algebra, and that preserved information can always be corrected. We also classify distinct operational criteria for preservation (e.g., 'noiseless','unitarily correctible', etc.) and introduce two natural criteria for measurement-stabilized and unconditionally preserved codes. Finally, for several of these operational criteria, we present efficient (polynomial in the state-space dimension) algorithms to find all of a channel's information-preserving structures.

The fusion of large scale classified side-scan sonar image mosaics.

Science.gov (United States)

Reed, Scott; Tena, Ruiz Ioseba; Capus, Chris; Petillot, Yvan

2006-07-01

This paper presents a unified framework for the creation of classified maps of the seafloor from sonar imagery. Significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed. The techniques described are directly applicable to a range of remote sensing problems. Recent advances in side-scan data correction are incorporated to compensate for the sonar beam pattern and motion of the acquisition platform. The corrected images are segmented using pixel-based textural features and standard classifiers. In parallel, the navigation of the sonar device is processed using Kalman filtering techniques. A simultaneous localization and mapping framework is adopted to improve the navigation accuracy and produce georeferenced mosaics of the segmented side-scan data. These are fused within a Markovian framework and two fusion models are presented. The first uses a voting scheme regularized by an isotropic Markov random field and is applicable when the reliability of each information source is unknown. The Markov model is also used to inpaint regions where no final classification decision can be reached using pixel level fusion. The second model formally introduces the reliability of each information source into a probabilistic model. Evaluation of the two models using both synthetic images and real data from a large scale survey shows significant quantitative and qualitative improvement using the fusion approach.

Correlation Dimension-Based Classifier

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel; Jiřina jr., M.

2014-01-01

Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014

An ensemble classifier to predict track geometry degradation

International Nuclear Information System (INIS)

Cárdenas-Gallo, Iván; Sarmiento, Carlos A.; Morales, Gilberto A.; Bolivar, Manuel A.; Akhavan-Tabatabaei, Raha

2017-01-01

Railway operations are inherently complex and source of several problems. In particular, track geometry defects are one of the leading causes of train accidents in the United States. This paper presents a solution approach which entails the construction of an ensemble classifier to forecast the degradation of track geometry. Our classifier is constructed by solving the problem from three different perspectives: deterioration, regression and classification. We considered a different model from each perspective and our results show that using an ensemble method improves the predictive performance. - Highlights: • We present an ensemble classifier to forecast the degradation of track geometry. • Our classifier considers three perspectives: deterioration, regression and classification. • We construct and test three models and our results show that using an ensemble method improves the predictive performance.

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

Directory of Open Access Journals (Sweden)

Yi Sun

2017-12-01

Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

Data characteristics that determine classifier performance

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2006-11-01

Full Text Available available at [11]. The kNN uses a LinearNN nearest neighbour search algorithm with an Euclidean distance metric [8]. The optimal k value is determined by performing 10-fold cross-validation. An optimal k value between 1 and 10 is used for Experiments 1... 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...

Pixel Classification of SAR ice images using ANFIS-PSO Classifier

Directory of Open Access Journals (Sweden)

G. Vasumathi

2016-12-01

Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

Science.gov (United States)

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Drosophila olfactory receptors as classifiers for volatiles from disparate real world applications

International Nuclear Information System (INIS)

Nowotny, Thomas; De Bruyne, Marien; Warr, Coral G; Berna, Amalia Z; Trowell, Stephen C

2014-01-01

Olfactory receptors evolved to provide animals with ecologically and behaviourally relevant information. The resulting extreme sensitivity and discrimination has proven useful to humans, who have therefore co-opted some animals’ sense of smell. One aim of machine olfaction research is to replace the use of animal noses and one avenue of such research aims to incorporate olfactory receptors into artificial noses. Here, we investigate how well the olfactory receptors of the fruit fly, Drosophila melanogaster, perform in classifying volatile odourants that they would not normally encounter. We collected a large number of in vivo recordings from individual Drosophila olfactory receptor neurons in response to an ecologically relevant set of 36 chemicals related to wine (‘wine set’) and an ecologically irrelevant set of 35 chemicals related to chemical hazards (‘industrial set’), each chemical at a single concentration. Resampled response sets were used to classify the chemicals against all others within each set, using a standard linear support vector machine classifier and a wrapper approach. Drosophila receptors appear highly capable of distinguishing chemicals that they have not evolved to process. In contrast to previous work with metal oxide sensors, Drosophila receptors achieved the best recognition accuracy if the outputs of all 20 receptor types were used. (paper)

Frog sound identification using extended k-nearest neighbor classifier

Science.gov (United States)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

A proposed defect tracking model for classifying the inserted defect reports to enhance software quality control.

Science.gov (United States)

Sultan, Torky; Khedr, Ayman E; Sayed, Mostafa

2013-01-01

NONE DECLARED Defect tracking systems play an important role in the software development organizations as they can store historical information about defects. There are many research in defect tracking models and systems to enhance their capabilities to be more specifically tracking, and were adopted with new technology. Furthermore, there are different studies in classifying bugs in a step by step method to have clear perception and applicable method in detecting such bugs. This paper shows a new proposed defect tracking model for the purpose of classifying the inserted defects reports in a step by step method for more enhancement of the software quality.

A Learning Outcome-Oriented Approach towards Classifying Pervasive Games for Learning Using Game Design Patterns and Contextual Information

Science.gov (United States)

Schmitz, Birgit; Klemke, Roland; Specht, Marcus

2013-01-01

Mobile and in particular pervasive games are a strong component of future scenarios for teaching and learning. Based on results from a previous review of practical papers, this work explores the educational potential of pervasive games for learning by analysing underlying game mechanisms. In order to determine and classify cognitive and affective…

Short text sentiment classification based on feature extension and ensemble classifier

Science.gov (United States)

Liu, Yang; Zhu, Xie

2018-05-01

With the rapid development of Internet social media, excavating the emotional tendencies of the short text information from the Internet, the acquisition of useful information has attracted the attention of researchers. At present, the commonly used can be attributed to the rule-based classification and statistical machine learning classification methods. Although micro-blog sentiment analysis has made good progress, there still exist some shortcomings such as not highly accurate enough and strong dependence from sentiment classification effect. Aiming at the characteristics of Chinese short texts, such as less information, sparse features, and diverse expressions, this paper considers expanding the original text by mining related semantic information from the reviews, forwarding and other related information. First, this paper uses Word2vec to compute word similarity to extend the feature words. And then uses an ensemble classifier composed of SVM, KNN and HMM to analyze the emotion of the short text of micro-blog. The experimental results show that the proposed method can make good use of the comment forwarding information to extend the original features. Compared with the traditional method, the accuracy, recall and F1 value obtained by this method have been improved.

32 CFR 147.15 - Guideline M-Misuse of Information technology systems.

Science.gov (United States)

2010-07-01

... 32 National Defense 1 2010-07-01 2010-07-01 false Guideline M-Misuse of Information technology... CLASSIFIED INFORMATION Adjudication § 147.15 Guideline M—Misuse of Information technology systems. (a) The... ability to properly protect classified systems, networks, and information. Information Technology Systems...

The Complete Gabor-Fisher Classifier for Robust Face Recognition

Directory of Open Access Journals (Sweden)

Štruc Vitomir

2010-01-01

Full Text Available Abstract This paper develops a novel face recognition technique called Complete Gabor Fisher Classifier (CGFC. Different from existing techniques that use Gabor filters for deriving the Gabor face representation, the proposed approach does not rely solely on Gabor magnitude information but effectively uses features computed based on Gabor phase information as well. It represents one of the few successful attempts found in the literature of combining Gabor magnitude and phase information for robust face recognition. The novelty of the proposed CGFC technique comes from (1 the introduction of a Gabor phase-based face representation and (2 the combination of the recognition technique using the proposed representation with classical Gabor magnitude-based methods into a unified framework. The proposed face recognition framework is assessed in a series of face verification and identification experiments performed on the XM2VTS, Extended YaleB, FERET, and AR databases. The results of the assessment suggest that the proposed technique clearly outperforms state-of-the-art face recognition techniques from the literature and that its performance is almost unaffected by the presence of partial occlusions of the facial area, changes in facial expression, or severe illumination changes.

CKD273, a new proteomics classifier assessing CKD and its prognosis.

Directory of Open Access Journals (Sweden)

Ángel Argilés

Full Text Available National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years as the main outcome measurements. None of the patients with CKD2730.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman. CKD273 was different in the groups with different renal function (p<0.003. The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.

Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

DEFF Research Database (Denmark)

Gajhede, Nicolai; Beck, Oliver; Purwins, Hendrik

2016-01-01

After having revolutionized image and speech processing, convolu- tional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples...

Three data partitioning strategies for building local classifiers (Chapter 14)

NARCIS (Netherlands)

Zliobaite, I.; Okun, O.; Valentini, G.; Re, M.

2011-01-01

Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize local expertise of individual classifiers. In this study we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training.

Robust Combining of Disparate Classifiers Through Order Statistics

Science.gov (United States)

Tumer, Kagan; Ghosh, Joydeep

2001-01-01

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

Knowledge Uncertainty and Composed Classifier

Czech Academy of Sciences Publication Activity Database

Klimešová, Dana; Ocelíková, E.

2007-01-01

Roč. 1, č. 2 (2007), s. 101-105 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Boosting architecture * contextual modelling * composed classifier * knowledge management, * knowledge * uncertainty Subject RIV: IN - Informatics, Computer Science

The decision tree classifier - Design and potential. [for Landsat-1 data

Science.gov (United States)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Representative Vector Machines: A Unified Framework for Classical Classifiers.

Science.gov (United States)

Gui, Jie; Liu, Tongliang; Tao, Dacheng; Sun, Zhenan; Tan, Tieniu

2016-08-01

Classifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) have been proposed in the literature. These typical and widely used classifiers were originally developed from different theory or application motivations and they are conventionally treated as independent and specific solutions for pattern classification. This paper proposes a novel pattern classification framework, namely, representative vector machines (or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are inspired in the framework of RVMs. For example, a robust pattern classification method called discriminant vector machine (DVM) is motivated from RVMs. Given a test example, DVM first finds its k -NNs and then performs classification based on the robust M-estimator and manifold regularization. Extensive experimental evaluations on a variety of visual recognition tasks such as face recognition (Yale and face recognition grand challenge databases), object categorization (Caltech-101 dataset), and action recognition (Action Similarity LAbeliNg) demonstrate the advantages of DVM over other classifiers.

76 FR 63811 - Structural Reforms To Improve the Security of Classified Networks and the Responsible Sharing and...

Science.gov (United States)

2011-10-13

... implementation of policies and minimum standards regarding information security, personnel security, and systems security; address both internal and external security threats and vulnerabilities; and provide policies and... policies and minimum standards will address all agencies that operate or access classified computer...

A cardiorespiratory classifier of voluntary and involuntary electrodermal activity

Directory of Open Access Journals (Sweden)

Sejdic Ervin

2010-02-01

Full Text Available Abstract Background Electrodermal reactions (EDRs can be attributed to many origins, including spontaneous fluctuations of electrodermal activity (EDA and stimuli such as deep inspirations, voluntary mental activity and startling events. In fields that use EDA as a measure of psychophysiological state, the fact that EDRs may be elicited from many different stimuli is often ignored. This study attempts to classify observed EDRs as voluntary (i.e., generated from intentional respiratory or mental activity or involuntary (i.e., generated from startling events or spontaneous electrodermal fluctuations. Methods Eight able-bodied participants were subjected to conditions that would cause a change in EDA: music imagery, startling noises, and deep inspirations. A user-centered cardiorespiratory classifier consisting of 1 an EDR detector, 2 a respiratory filter and 3 a cardiorespiratory filter was developed to automatically detect a participant's EDRs and to classify the origin of their stimulation as voluntary or involuntary. Results Detected EDRs were classified with a positive predictive value of 78%, a negative predictive value of 81% and an overall accuracy of 78%. Without the classifier, EDRs could only be correctly attributed as voluntary or involuntary with an accuracy of 50%. Conclusions The proposed classifier may enable investigators to form more accurate interpretations of electrodermal activity as a measure of an individual's psychophysiological state.

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier

NARCIS (Netherlands)

Hashemi, H.; Tax, D.M.J.; Duin, R.P.W.; Javaherian, A.; De Groot, P.

2008-01-01

Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a

The Effects of information barrier requirements on the trilateral initiative attribute measurement system (AVNG)

International Nuclear Information System (INIS)

MacArthur, D.W.; Langner, D.C.; Whiteson, R.; Wolford, J.K.

2001-01-01

Although the detection techniques used for measuring classified materials are very similar to those used in unclassified measurements, the surrounding packaging is generally very different. If iZ classified item is to be measured, an information barrier is required to protect any classified data acquired. This information barrier must protect the classified information while giving the inspector confidence that the unclassified outputs accurately reflect the classified inputs, Both information barrier and authentication considerations must be considered during all phases of system design and fabrication. One example of such a measurement system is the attribute measurement system (termed the AVNG) designed for the: Trilateral Initiative. We will discuss the integration of information barrier components into this system as well as the effects of an information barrier (including authentication) concerns on the implementation of the detector systems.

Bayesian Classifier for Medical Data from Doppler Unit

Directory of Open Access Journals (Sweden)

J. Málek

2006-01-01

Full Text Available Nowadays, hand-held ultrasonic Doppler units (probes are often used for noninvasive screening of atherosclerosis in the arteries of the lower limbs. The mean velocity of blood flow in time and blood pressures are measured on several positions on each lower limb. By listening to the acoustic signal generated by the device or by reading the signal displayed on screen, a specialist can detect peripheral arterial disease (PAD.This project aims to design software that will be able to analyze data from such a device and classify it into several diagnostic classes. At the Department of Functional Diagnostics at the Regional Hospital in Liberec a database of several hundreds signals was collected. In cooperation with the specialist, the signals were manually classified into four classes. For each class, selected signal features were extracted and then used for training a Bayesian classifier. Another set of signals was used for evaluating and optimizing the parameters of the classifier. Slightly above 84 % of successfully recognized diagnostic states, was recently achieved on the test data. 

Classifying Linear Canonical Relations

OpenAIRE

Lorand, Jonathan

2015-01-01

In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.

Classified facilities for environmental protection

International Nuclear Information System (INIS)

Anon.

1993-02-01

The legislation of the classified facilities governs most of the dangerous or polluting industries or fixed activities. It rests on the law of 9 July 1976 concerning facilities classified for environmental protection and its application decree of 21 September 1977. This legislation, the general texts of which appear in this volume 1, aims to prevent all the risks and the harmful effects coming from an installation (air, water or soil pollutions, wastes, even aesthetic breaches). The polluting or dangerous activities are defined in a list called nomenclature which subjects the facilities to a declaration or an authorization procedure. The authorization is delivered by the prefect at the end of an open and contradictory procedure after a public survey. In addition, the facilities can be subjected to technical regulations fixed by the Environment Minister (volume 2) or by the prefect for facilities subjected to declaration (volume 3). (A.B.)

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.

Development of a simple method for classifying the degree of importance of components in nuclear power plants using probabilistic analysis technique

International Nuclear Information System (INIS)

Shimada, Yoshio; Miyazaki, Takamasa

2006-01-01

In order to analyze large amounts of trouble information of overseas nuclear power plants, it is necessary to select information that is significant in terms of both safety and reliability. In this research, a method of efficiently and simply classifying degrees of importance of components in terms of safety and reliability while paying attention to root-cause components appearing in the information was developed. Regarding safety, the reactor core damage frequency (CDF), which is used in the probabilistic analysis of a reactor, was used. Regarding reliability, the automatic plant trip probability (APTP), which is used in the probabilistic analysis of automatic reactor trips, was used. These two aspects were reflected in the development of criteria for classifying degrees of importance of components. By applying these criteria, a method of quantitatively and simply judging the significance of trouble information of overseas nuclear power plants was developed. (author)

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Directory of Open Access Journals (Sweden)

Eils Roland

2006-06-01

Full Text Available Abstract Background The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. Conclusion This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

Fisher classifier and its probability of error estimation

Science.gov (United States)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Application of a naive Bayesians classifiers in assessing the supplier

Directory of Open Access Journals (Sweden)

Mijailović Snežana

2017-01-01

Full Text Available The paper considers the class of interactive knowledge based systems whose main purpose of making proposals and assisting customers in making decisions. The mathematical model provides a set of examples of learning about the delivered series of outflows from three suppliers, as well as an analysis of an illustrative example for assessing the supplier using a naive Bayesian classifier. The model was developed on the basis of the analysis of subjective probabilities, which are later revised with the help of new empirical information and Bayesian theorem on a posterior probability, i.e. combining of subjective and objective conditional probabilities in the choice of a reliable supplier.

The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

International Nuclear Information System (INIS)

Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

2014-01-01

We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.

Reducing variability in the output of pattern classifiers using histogram shaping

International Nuclear Information System (INIS)

Gupta, Shalini; Kan, Chih-Wen; Markey, Mia K.

2010-01-01

Purpose: The authors present a novel technique based on histogram shaping to reduce the variability in the output and (sensitivity, specificity) pairs of pattern classifiers with identical ROC curves, but differently distributed outputs. Methods: The authors identify different sources of variability in the output of linear pattern classifiers with identical ROC curves, which also result in classifiers with differently distributed outputs. They theoretically develop a novel technique based on the matching of the histograms of these differently distributed pattern classifier outputs to reduce the variability in their (sensitivity, specificity) pairs at fixed decision thresholds, and to reduce the variability in their actual output values. They empirically demonstrate the efficacy of the proposed technique by means of analyses on the simulated data and real world mammography data. Results: For the simulated data, with three different known sources of variability, and for the real world mammography data with unknown sources of variability, the proposed classifier output calibration technique significantly reduced the variability in the classifiers' (sensitivity, specificity) pairs at fixed decision thresholds. Furthermore, for classifiers with monotonically or approximately monotonically related output variables, the histogram shaping technique also significantly reduced the variability in their actual output values. Conclusions: Classifier output calibration based on histogram shaping can be successfully employed to reduce the variability in the output values and (sensitivity, specificity) pairs of pattern classifiers with identical ROC curves, but differently distributed outputs.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

Science.gov (United States)

Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

2014-03-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

International Nuclear Information System (INIS)

Blanco, A; Rodriguez, R; Martinez-Maranon, I

2014-01-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity

Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes

NARCIS (Netherlands)

Smits, M.; Janssen, J.; Vet, de H.C.W.; Zwaan, L.; Timmermans, D.R.M.; Groenewegen, P.P.; Wagner, C.

2009-01-01

BACKGROUND: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis: is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

Analysis of unintended events in hospitals : inter-rater reliability of constructing causal trees and classifying root causes

NARCIS (Netherlands)

Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Groenewegen, P.P.; Timmermans, D.

2009-01-01

Background. Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes.

NARCIS (Netherlands)

Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Timmermans, D.; Groenewegen, P.; Wagner, C.

2009-01-01

Background: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

Science.gov (United States)

Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

2018-06-13

Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

Classifying sows' activity types from acceleration patterns

DEFF Research Database (Denmark)

Cornou, Cecile; Lundbye-Christensen, Søren

2008-01-01

An automated method of classifying sow activity using acceleration measurements would allow the individual sow's behavior to be monitored throughout the reproductive cycle; applications for detecting behaviors characteristic of estrus and farrowing or to monitor illness and welfare can be foreseen....... This article suggests a method of classifying five types of activity exhibited by group-housed sows. The method involves the measurement of acceleration in three dimensions. The five activities are: feeding, walking, rooting, lying laterally and lying sternally. Four time series of acceleration (the three...

Naive Bayesian classifiers for multinomial features: a theoretical analysis

CSIR Research Space (South Africa)

Van Dyk, E

2007-11-01

Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...

Scientific and technological information: analysis of periodic publications of information science

OpenAIRE

Mayara Cintya do Nascimento Vasconcelos; Gabriela Belmont de Farias

2017-01-01

The research analyzes the articles published in national scientific journals of the area of Information Science, classified with Qualis A1, having as parameter the term "scientific and technological information". It presents concepts about scientific and technological information and the processes that involve its uses, as well as scientific communication, information flows and sources of information. The methodology used is a descriptive study with a quantitative-qualitative approach, using ...

Categorization of Information is the First Step to Enterprise Information Security

Directory of Open Access Journals (Sweden)

M. M. Koptenkov

2011-12-01

Full Text Available This article reveals the necessity and importance of information categorizing in the area of information security systems development. This paper describes an information categorizing method based on the assessment of damages in a breach of confidentiality, integrity and availability of processed information. This method allows us to classify information on the degree of its value to the organization and most effectively implement an access control system.

Automatic discrimination between safe and unsafe swallowing using a reputation-based classifier

Directory of Open Access Journals (Sweden)

Nikjoo Mohammad S

2011-11-01

Full Text Available Abstract Background Swallowing accelerometry has been suggested as a potential non-invasive tool for bedside dysphagia screening. Various vibratory signal features and complementary measurement modalities have been put forth in the literature for the potential discrimination between safe and unsafe swallowing. To date, automatic classification of swallowing accelerometry has exclusively involved a single-axis of vibration although a second axis is known to contain additional information about the nature of the swallow. Furthermore, the only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations. Methods In this paper, a large corpus of dual-axis accelerometric signals were collected from 30 older adults (aged 65.47 ± 13.4 years, 15 male referred to videofluoroscopic examination on the suspicion of dysphagia. We invoked a reputation-based classifier combination to automatically categorize the dual-axis accelerometric signals into safe and unsafe swallows, as labeled via videofluoroscopic review. From these participants, a total of 224 swallowing samples were obtained, 164 of which were labeled as unsafe swallows (swallows where the bolus entered the airway and 60 as safe swallows. Three separate support vector machine (SVM classifiers and eight different features were selected for classification. Results With selected time, frequency and information theoretic features, the reputation-based algorithm distinguished between safe and unsafe swallowing with promising accuracy (80.48 ± 5.0%, high sensitivity (97.1 ± 2% and modest specificity (64 ± 8.8%. Interpretation of the most discriminatory features revealed that in general, unsafe swallows had lower mean vibration amplitude and faster autocorrelation decay, suggestive of decreased hyoid excursion and compromised coordination, respectively. Further, owing to its performance-based weighting of component classifiers, the static

An Improved Fast Compressive Tracking Algorithm Based on Online Random Forest Classifier

Directory of Open Access Journals (Sweden)

Xiong Jintao

2016-01-01

Full Text Available The fast compressive tracking (FCT algorithm is a simple and efficient algorithm, which is proposed in recent years. But, it is difficult to deal with the factors such as occlusion, appearance changes, pose variation, etc in processing. The reasons are that, Firstly, even if the naive Bayes classifier is fast in training, it is not robust concerning the noise. Secondly, the parameters are required to vary with the unique environment for accurate tracking. In this paper, we propose an improved fast compressive tracking algorithm based on online random forest (FCT-ORF for robust visual tracking. Firstly, we combine ideas with the adaptive compressive sensing theory regarding the weighted random projection to exploit both local and discriminative information of the object. The second reason is the online random forest classifier for online tracking which is demonstrated with more robust to the noise adaptively and high computational efficiency. The experimental results show that the algorithm we have proposed has a better performance in the field of occlusion, appearance changes, and pose variation than the fast compressive tracking algorithm’s contribution.

SVM classifier on chip for melanoma detection.

Science.gov (United States)

Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak

2017-07-01

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

Rich in vitamin C or just a convenient snack? Multiple-category reasoning with cross-classified foods.

Science.gov (United States)

Hayes, Brett K; Kurniawan, Hendy; Newell, Ben R

2011-01-01

Two studies examined multiple category reasoning in property induction with cross-classified foods. Pilot tests identified foods that were more typical of a taxonomic category (e.g., "fruit"; termed 'taxonomic primary') or a script based category (e.g., "snack foods"; termed 'script primary'). They also confirmed that taxonomic categories were perceived as more coherent than script categories. In Experiment 1 participants completed an induction task in which information from multiple categories could be searched and combined to generate a property prediction about a target food. Multiple categories were more often consulted and used in prediction for script primary than for taxonomic primary foods. Experiment 2 replicated this finding across a range of property types but found that multiple category reasoning was reduced in the presence of a concurrent cognitive load. Property type affected which categories were consulted first and how information from multiple categories was weighted. The results show that multiple categories are more likely to be used for property predictions about cross-classified objects when an object is primarily associated with a category that has low coherence.

Performance of classification confidence measures in dynamic classifier systems

Czech Academy of Sciences Publication Activity Database

Štefka, D.; Holeňa, Martin

2013-01-01

Roč. 23, č. 4 (2013), s. 299-319 ISSN 1210-0552 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : classifier combining * dynamic classifier systems * classification confidence Subject RIV: IN - Informatics, Computer Science Impact factor: 0.412, year: 2013

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

Directory of Open Access Journals (Sweden)

Zhang Xinzheng

2017-10-01

Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy.

Directory of Open Access Journals (Sweden)

Lina Zhang

Full Text Available Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information, PSSM (Position Specific Scoring Matrix, RSA (Relative Solvent Accessibility, and CTD (Composition, Transition, Distribution. The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest, SMO (Sequential Minimal Optimization, NNA (Nearest Neighbor Algorithm, and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew's Correlation Coefficient of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc.

Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

Science.gov (United States)

Akhtar, Naveed; Mian, Ajmal

2017-10-03

We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

Classifying spaces with virtually cyclic stabilizers for linear groups

DEFF Research Database (Denmark)

Degrijse, Dieter Dries; Köhl, Ralf; Petrosyan, Nansen

2015-01-01

We show that every discrete subgroup of GL(n, ℝ) admits a finite-dimensional classifying space with virtually cyclic stabilizers. Applying our methods to SL(3, ℤ), we obtain a four-dimensional classifying space with virtually cyclic stabilizers and a decomposition of the algebraic K-theory of its...

Intuitive Action Set Formation in Learning Classifier Systems with Memory Registers

NARCIS (Netherlands)

Simões, L.F.; Schut, M.C.; Haasdijk, E.W.

2008-01-01

An important design goal in Learning Classifier Systems (LCS) is to equally reinforce those classifiers which cause the level of reward supplied by the environment. In this paper, we propose a new method for action set formation in LCS. When applied to a Zeroth Level Classifier System with Memory

Design of Robust Neural Network Classifiers

DEFF Research Database (Denmark)

Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads

1998-01-01

This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...

A radial basis classifier for the automatic detection of aspiration in children with dysphagia

Directory of Open Access Journals (Sweden)

Blain Stefanie

2006-07-01

Full Text Available Abstract Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion

Combining multiple classifiers for age classification

CSIR Research Space (South Africa)

Van Heerden, C

2009-11-01

Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...

Current Directional Protection of Series Compensated Line Using Intelligent Classifier

Directory of Open Access Journals (Sweden)

M. Mollanezhad Heydarabadi

2016-12-01

Full Text Available Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses only half cycle of pre-fault and post fault current samples at relay location to feed the classifier. A lot of forward and backward fault simulations under different system conditions upon a transmission line with a fixed series capacitor are carried out using PSCAD/EMTDC software. The applicability of decision tree (DT, probabilistic neural network (PNN and support vector machine (SVM are investigated using simulated data under different system conditions. The performance comparison of the classifiers indicates that the SVM is a best suitable classifier for fault direction discriminating. The backward faults can be accurately distinguished from forward faults even under current inversion without require to detect of the current inversion condition.

Obscenity detection using haar-like features and Gentle Adaboost classifier.

Science.gov (United States)

Mustafa, Rashed; Min, Yang; Zhu, Dingju

2014-01-01

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

Directory of Open Access Journals (Sweden)

Rashed Mustafa

2014-01-01

Full Text Available Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

REPTREE CLASSIFIER FOR IDENTIFYING LINK SPAM IN WEB SEARCH ENGINES

Directory of Open Access Journals (Sweden)

S.K. Jayanthi

2013-01-01

Full Text Available Search Engines are used for retrieving the information from the web. Most of the times, the importance is laid on top 10 results sometimes it may shrink as top 5, because of the time constraint and reliability on the search engines. Users believe that top 10 or 5 of total results are more relevant. Here comes the problem of spamdexing. It is a method to deceive the search result quality. Falsified metrics such as inserting enormous amount of keywords or links in website may take that website to the top 10 or 5 positions. This paper proposes a classifier based on the Reptree (Regression tree representative. As an initial step Link-based features such as neighbors, pagerank, truncated pagerank, trustrank and assortativity related attributes are inferred. Based on this features, tree is constructed. The tree uses the feature inference to differentiate spam sites from legitimate sites. WEBSPAM-UK-2007 dataset is taken as a base. It is preprocessed and converted into five datasets FEATA, FEATB, FEATC, FEATD and FEATE. Only link based features are taken for experiments. This paper focus on link spam alone. Finally a representative tree is created which will more precisely classify the web spam entries. Results are given. Regression tree classification seems to perform well as shown through experiments.

Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing.

Science.gov (United States)

Riniker, Sereina; Fechner, Nikolas; Landrum, Gregory A

2013-11-25

The concept of data fusion - the combination of information from different sources describing the same object with the expectation to generate a more accurate representation - has found application in a very broad range of disciplines. In the context of ligand-based virtual screening (VS), data fusion has been applied to combine knowledge from either different active molecules or different fingerprints to improve similarity search performance. Machine-learning (ML) methods based on fusion of multiple homogeneous classifiers, in particular random forests, have also been widely applied in the ML literature. The heterogeneous version of classifier fusion - fusing the predictions from different model types - has been less explored. Here, we investigate heterogeneous classifier fusion for ligand-based VS using three different ML methods, RF, naïve Bayes (NB), and logistic regression (LR), with four 2D fingerprints, atom pairs, topological torsions, RDKit fingerprint, and circular fingerprint. The methods are compared using a previously developed benchmarking platform for 2D fingerprints which is extended to ML methods in this article. The original data sets are filtered for difficulty, and a new set of challenging data sets from ChEMBL is added. Data sets were also generated for a second use case: starting from a small set of related actives instead of diverse actives. The final fused model consistently outperforms the other approaches across the broad variety of targets studied, indicating that heterogeneous classifier fusion is a very promising approach for ligand-based VS. The new data sets together with the adapted source code for ML methods are provided in the Supporting Information .

A systems biology-based classifier for hepatocellular carcinoma diagnosis.

Directory of Open Access Journals (Sweden)

Yanqiong Zhang

Full Text Available AIM: The diagnosis of hepatocellular carcinoma (HCC in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71% and area under ROC curve (approximating 1.0, and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.

A Customizable Text Classifier for Text Mining

Directory of Open Access Journals (Sweden)

Yun-liang Zhang

2007-12-01

Full Text Available Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically or be adjusted by user. The user can also control the number of domains chosen and decide the standard with which to choose the texts based on demand and abundance of materials. The performance of the classifier varies with the user's choice.

A survey of decision tree classifier methodology

Science.gov (United States)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

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M. Al-Rousan

2005-08-01

Full Text Available Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Comparison of Classifier Architectures for Online Neural Spike Sorting.

Science.gov (United States)

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

Higher School Marketing Strategy Formation: Classifying the Factors

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N. K. Shemetova

2012-01-01

Full Text Available The paper deals with the main trends of higher school management strategy formation. The author specifies the educational changes in the modern information society determining the strategy options. For each professional training level the author denotes the set of strategic factors affecting the educational service consumers and, therefore, the effectiveness of the higher school marketing. The given factors are classified from the stand-points of the providers and consumers of educational service (enrollees, students, graduates and postgraduates. The research methods include the statistic analysis and general methods of scientific analysis, synthesis, induction, deduction, comparison, and classification. The author is convinced that the university management should develop the necessary prerequisites for raising the graduates’ competitiveness in the labor market, and stimulate the active marketing policies of the relating subdivisions and departments. In author’s opinion, the above classification of marketing strategy factors can be used as the system of values for educational service providers. 

Learning multiscale and deep representations for classifying remotely sensed imagery

Science.gov (United States)

Zhao, Wenzhi; Du, Shihong

2016-03-01

It is widely agreed that spatial features can be combined with spectral properties for improving interpretation performances on very-high-resolution (VHR) images in urban areas. However, many existing methods for extracting spatial features can only generate low-level features and consider limited scales, leading to unpleasant classification results. In this study, multiscale convolutional neural network (MCNN) algorithm was presented to learn spatial-related deep features for hyperspectral remote imagery classification. Unlike traditional methods for extracting spatial features, the MCNN first transforms the original data sets into a pyramid structure containing spatial information at multiple scales, and then automatically extracts high-level spatial features using multiscale training data sets. Specifically, the MCNN has two merits: (1) high-level spatial features can be effectively learned by using the hierarchical learning structure and (2) multiscale learning scheme can capture contextual information at different scales. To evaluate the effectiveness of the proposed approach, the MCNN was applied to classify the well-known hyperspectral data sets and compared with traditional methods. The experimental results shown a significant increase in classification accuracies especially for urban areas.

An automatic classifier of emotions built from entropy of noise.

Science.gov (United States)

Ferreira, Jacqueline; Brás, Susana; Silva, Carlos F; Soares, Sandra C

2017-04-01

The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion. © 2016 Society for Psychophysiological Research.

Collaborative Information Filtering in Cooperative Communities.

Science.gov (United States)

Okamoto, T.; Miyahara, K.

1998-01-01

The purpose of this study was to develop an information filtering system which collects, classifies, selects, and stores various kinds of information found through the Internet. A collaborative form of information gathering was examined and a model was built and implemented in the Internet information space. (AEF)

A novel statistical method for classifying habitat generalists and specialists

DEFF Research Database (Denmark)

Chazdon, Robin L; Chao, Anne; Colwell, Robert K

2011-01-01

in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher......: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance...... fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest...

The Nutraceutical Bioavailability Classification Scheme: Classifying Nutraceuticals According to Factors Limiting their Oral Bioavailability.

Science.gov (United States)

McClements, David Julian; Li, Fang; Xiao, Hang

2015-01-01

The oral bioavailability of a health-promoting dietary component (nutraceutical) may be limited by various physicochemical and physiological phenomena: liberation from food matrices, solubility in gastrointestinal fluids, interaction with gastrointestinal components, chemical degradation or metabolism, and epithelium cell permeability. Nutraceutical bioavailability can therefore be improved by designing food matrices that control their bioaccessibility (B*), absorption (A*), and transformation (T*) within the gastrointestinal tract (GIT). This article reviews the major factors influencing the gastrointestinal fate of nutraceuticals, and then uses this information to develop a new scheme to classify the major factors limiting nutraceutical bioavailability: the nutraceutical bioavailability classification scheme (NuBACS). This new scheme is analogous to the biopharmaceutical classification scheme (BCS) used by the pharmaceutical industry to classify drug bioavailability, but it contains additional factors important for understanding nutraceutical bioavailability in foods. The article also highlights potential strategies for increasing the oral bioavailability of nutraceuticals based on their NuBACS designation (B*A*T*).

Ensemble of classifiers based network intrusion detection system performance bound

CSIR Research Space (South Africa)

Mkuzangwe, Nenekazi NP

2017-11-01

Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

Bayes classifiers for imbalanced traffic accidents datasets.

Science.gov (United States)

Mujalli, Randa Oqab; López, Griselda; Garach, Laura

2016-03-01

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

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Shirui Huo

2017-01-01

Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

Implications of physical symmetries in adaptive image classifiers

DEFF Research Database (Denmark)

Sams, Thomas; Hansen, Jonas Lundbek

2000-01-01

It is demonstrated that rotational invariance and reflection symmetry of image classifiers lead to a reduction in the number of free parameters in the classifier. When used in adaptive detectors, e.g. neural networks, this may be used to decrease the number of training samples necessary to learn...... a given classification task, or to improve generalization of the neural network. Notably, the symmetrization of the detector does not compromise the ability to distinguish objects that break the symmetry. (C) 2000 Elsevier Science Ltd. All rights reserved....

Bitcoin and Beyond: Exclusively Informational Money

NARCIS (Netherlands)

Bergstra, J.A.; de Leeuw, K.

2013-01-01

The famous new money Bitcoin is classified as a technical informational money (TIM). Besides introducing the idea of a TIM, a more extreme notion of informational money will be developed: exclusively informational money (EXIM). The informational coins (INCOs) of an EXIM can be in control of an agent

Optimization of short amino acid sequences classifier

Science.gov (United States)

Barcz, Aleksy; Szymański, Zbigniew

This article describes processing methods used for short amino acid sequences classification. The data processed are 9-symbols string representations of amino acid sequences, divided into 49 data sets - each one containing samples labeled as reacting or not with given enzyme. The goal of the classification is to determine for a single enzyme, whether an amino acid sequence would react with it or not. Each data set is processed separately. Feature selection is performed to reduce the number of dimensions for each data set. The method used for feature selection consists of two phases. During the first phase, significant positions are selected using Classification and Regression Trees. Afterwards, symbols appearing at the selected positions are substituted with numeric values of amino acid properties taken from the AAindex database. In the second phase the new set of features is reduced using a correlation-based ranking formula and Gram-Schmidt orthogonalization. Finally, the preprocessed data is used for training LS-SVM classifiers. SPDE, an evolutionary algorithm, is used to obtain optimal hyperparameters for the LS-SVM classifier, such as error penalty parameter C and kernel-specific hyperparameters. A simple score penalty is used to adapt the SPDE algorithm to the task of selecting classifiers with best performance measures values.

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

Energy Technology Data Exchange (ETDEWEB)

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom [Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Lynch, David A. [Department of Radiology, National Jewish Medical and Research Center, Denver, Colorado 80206 (United States)

2013-05-15

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

International Nuclear Information System (INIS)

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom; Lynch, David A.

2013-01-01

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 × 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs—normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI

Information theoretic description of networks

Science.gov (United States)

Wilhelm, Thomas; Hollunder, Jens

2007-11-01

We present a new information theoretic approach for network characterizations. It is developed to describe the general type of networks with n nodes and L directed and weighted links, i.e., it also works for the simpler undirected and unweighted networks. The new information theoretic measures for network characterizations are based on a transmitter-receiver analogy of effluxes and influxes. Based on these measures, we classify networks as either complex or non-complex and as either democracy or dictatorship networks. Directed networks, in particular, are furthermore classified as either information spreading and information collecting networks. The complexity classification is based on the information theoretic network complexity measure medium articulation (MA). It is proven that special networks with a medium number of links ( L∼n1.5) show the theoretical maximum complexity MA=(log n)2/2. A network is complex if its MA is larger than the average MA of appropriately randomized networks: MA>MAr. A network is of the democracy type if its redundancy Rdictatorship network. In democracy networks all nodes are, on average, of similar importance, whereas in dictatorship networks some nodes play distinguished roles in network functioning. In other words, democracy networks are characterized by cycling of information (or mass, or energy), while in dictatorship networks there is a straight through-flow from sources to sinks. The classification of directed networks into information spreading and information collecting networks is based on the conditional entropies of the considered networks ( H(A/B)=uncertainty of sender node if receiver node is known, H(B/A)=uncertainty of receiver node if sender node is known): if H(A/B)>H(B/A), it is an information collecting network, otherwise an information spreading network. Finally, different real networks (directed and undirected, weighted and unweighted) are classified according to our general scheme.

Silicon nanowire arrays as learning chemical vapour classifiers

International Nuclear Information System (INIS)

Niskanen, A O; Colli, A; White, R; Li, H W; Spigone, E; Kivioja, J M

2011-01-01

Nanowire field-effect transistors are a promising class of devices for various sensing applications. Apart from detecting individual chemical or biological analytes, it is especially interesting to use multiple selective sensors to look at their collective response in order to perform classification into predetermined categories. We show that non-functionalised silicon nanowire arrays can be used to robustly classify different chemical vapours using simple statistical machine learning methods. We were able to distinguish between acetone, ethanol and water with 100% accuracy while methanol, ethanol and 2-propanol were classified with 96% accuracy in ambient conditions.

Classified Component Disposal at the Nevada National Security Site (NNSS) - 13454

Energy Technology Data Exchange (ETDEWEB)

Poling, Jeanne; Arnold, Pat [National Security Technologies, LLC (NSTec), P.O. Box 98521, Las Vegas, NV 89193-8521 (United States); Saad, Max [Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185 (United States); DiSanza, Frank [E. Frank DiSanza Consulting, 2250 Alanhurst Drive, Henderson, NV 89052 (United States); Cabble, Kevin [U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, P.O. Box 98518, Las Vegas, NV 89193-8518 (United States)

2013-07-01

The Nevada National Security Site (NNSS) has added the capability needed for the safe, secure disposal of non-nuclear classified components that have been declared excess to national security requirements. The NNSS has worked with U.S. Department of Energy, National Nuclear Security Administration senior leadership to gain formal approval for permanent burial of classified matter at the NNSS in the Area 5 Radioactive Waste Management Complex owned by the U.S. Department of Energy. Additionally, by working with state regulators, the NNSS added the capability to dispose non-radioactive hazardous and non-hazardous classified components. The NNSS successfully piloted the new disposal pathway with the receipt of classified materials from the Kansas City Plant in March 2012. (authors)

Classified Component Disposal at the Nevada National Security Site (NNSS) - 13454

International Nuclear Information System (INIS)

Poling, Jeanne; Arnold, Pat; Saad, Max; DiSanza, Frank; Cabble, Kevin

2013-01-01

The Nevada National Security Site (NNSS) has added the capability needed for the safe, secure disposal of non-nuclear classified components that have been declared excess to national security requirements. The NNSS has worked with U.S. Department of Energy, National Nuclear Security Administration senior leadership to gain formal approval for permanent burial of classified matter at the NNSS in the Area 5 Radioactive Waste Management Complex owned by the U.S. Department of Energy. Additionally, by working with state regulators, the NNSS added the capability to dispose non-radioactive hazardous and non-hazardous classified components. The NNSS successfully piloted the new disposal pathway with the receipt of classified materials from the Kansas City Plant in March 2012. (authors)

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.

Generalization in the XCSF classifier system: analysis, improvement, and extension.

Science.gov (United States)

Lanzi, Pier Luca; Loiacono, Daniele; Wilson, Stewart W; Goldberg, David E

2007-01-01

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.

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.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

Energy Technology Data Exchange (ETDEWEB)

Baraldi, Piero, E-mail: piero.baraldi@polimi.i [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

2011-04-15

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

International Nuclear Information System (INIS)

Baraldi, Piero; Razavi-Far, Roozbeh; Zio, Enrico

2011-01-01

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

76 FR 10262 - Information Security Program

Science.gov (United States)

2011-02-24

... FEDERAL MARITIME COMMISSION 46 CFR Part 503 [Docket No. 11-01] RIN 3072-AC40 Information Security... (FMC or Commission) amends its regulations relating to its Information Security Program to reflect the changes implemented by Executive Order 13526--Classified National Security Information--that took effect...

Iceberg Semantics For Count Nouns And Mass Nouns: Classifiers, measures and portions

Directory of Open Access Journals (Sweden)

Fred Landman

2016-12-01

It is the analysis of complex NPs and their mass-count properties that is the focus of the second part of this paper. There I develop an analysis of English and Dutch pseudo- partitives, in particular, measure phrases like three liters of wine and classifier phrases like three glasses of wine. We will study measure interpretations and classifier interpretations of measures and classifiers, and different types of classifier interpretations: container interpretations, contents interpretations, and - indeed - portion interpretations. Rothstein 2011 argues that classifier interpretations (including portion interpretations of pseudo partitives pattern with count nouns, but that measure interpretations pattern with mass nouns. I will show that this distinction follows from the very basic architecture of Iceberg semantics.

Human Activity Recognition by Combining a Small Number of Classifiers.

Science.gov (United States)

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

Science.gov (United States)

Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

2017-01-01

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

Classifying web pages with visual features

NARCIS (Netherlands)

de Boer, V.; van Someren, M.; Lupascu, T.; Filipe, J.; Cordeiro, J.

2010-01-01

To automatically classify and process web pages, current systems use the textual content of those pages, including both the displayed content and the underlying (HTML) code. However, a very important feature of a web page is its visual appearance. In this paper, we show that using generic visual

Dynamic integration of classifiers in the space of principal components

NARCIS (Netherlands)

Tsymbal, A.; Pechenizkiy, M.; Puuronen, S.; Patterson, D.W.; Kalinichenko, L.A.; Manthey, R.; Thalheim, B.; Wloka, U.

2003-01-01

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

Science.gov (United States)

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-11-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

78 FR 48037 - Facility Security Clearance and Safeguarding of National Security Information and Restricted Data

Science.gov (United States)

2013-08-07

... Clearance and Safeguarding of National Security Information and Restricted Data AGENCY: Nuclear Regulatory... the objectives of Executive Order 13526, Classified National Security Information. The rule allows... signed Executive Order 13526, Classified National Security Information, which was published in the...

Classifying Radio Galaxies with the Convolutional Neural Network

Energy Technology Data Exchange (ETDEWEB)

Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

2017-06-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

Classifying Radio Galaxies with the Convolutional Neural Network

Science.gov (United States)

Aniyan, A. K.; Thorat, K.

2017-06-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

Classifying Radio Galaxies with the Convolutional Neural Network

International Nuclear Information System (INIS)

Aniyan, A. K.; Thorat, K.

2017-01-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

An expert computer program for classifying stars on the MK spectral classification system

International Nuclear Information System (INIS)

Gray, R. O.; Corbally, C. J.

2014-01-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

An expert computer program for classifying stars on the MK spectral classification system

Energy Technology Data Exchange (ETDEWEB)

Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 26808 (United States); Corbally, C. J. [Vatican Observatory Research Group, Tucson, AZ 85721-0065 (United States)

2014-04-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors.

Science.gov (United States)

Gustafsson, Mats G; Wallman, Mikael; Wickenberg Bolin, Ulrika; Göransson, Hanna; Fryknäs, M; Andersson, Claes R; Isaksson, Anders

2010-06-01

Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (CI) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the CI is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice. It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples. Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets. An empirically derived ME prior seems promising for improving the Bayesian CI for the unknown error rate of a designed classifier. Copyright 2010 Elsevier B.V. All rights reserved.

Multi-feature classifiers for burst detection in single EEG channels from preterm infants

Science.gov (United States)

Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

2017-08-01

Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

From gesture to sign language: conventionalization of classifier constructions by adult hearing learners of British Sign Language.

Science.gov (United States)

Marshall, Chloë R; Morgan, Gary

2015-01-01

There has long been interest in why languages are shaped the way they are, and in the relationship between sign language and gesture. In sign languages, entity classifiers are handshapes that encode how objects move, how they are located relative to one another, and how multiple objects of the same type are distributed in space. Previous studies have shown that hearing adults who are asked to use only manual gestures to describe how objects move in space will use gestures that bear some similarities to classifiers. We investigated how accurately hearing adults, who had been learning British Sign Language (BSL) for 1-3 years, produce and comprehend classifiers in (static) locative and distributive constructions. In a production task, learners of BSL knew that they could use their hands to represent objects, but they had difficulty choosing the same, conventionalized, handshapes as native signers. They were, however, highly accurate at encoding location and orientation information. Learners therefore show the same pattern found in sign-naïve gesturers. In contrast, handshape, orientation, and location were comprehended with equal (high) accuracy, and testing a group of sign-naïve adults showed that they too were able to understand classifiers with higher than chance accuracy. We conclude that adult learners of BSL bring their visuo-spatial knowledge and gestural abilities to the tasks of understanding and producing constructions that contain entity classifiers. We speculate that investigating the time course of adult sign language acquisition might shed light on how gesture became (and, indeed, becomes) conventionalized during the genesis of sign languages. Copyright © 2014 Cognitive Science Society, Inc.

Aplikasi E-Tour Guide dengan Fitur Pengenalan Image Menggunakan Metode Haar Classifier

Directory of Open Access Journals (Sweden)

Derwin Suhartono

2013-12-01

Full Text Available Smartphone has became an important instrument in modern society as it is used for entertainment and information searching except for communication. Concerning to this condition, it is needed to develop an application in order to improve smart phone functionality. The objective of this research is to create an application named E-Tour Guide as a tool for helping to plan and manage tourism activity equipped with image recognition feature. Image recognition method used is the Haar Classifier method. The feature is used to recognize historical objects. From the testing result done to 20 images sample, 85% accuracy is achieved for the image recognition feature.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

Science.gov (United States)

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

Directory of Open Access Journals (Sweden)

Bin Li

2012-02-01

Full Text Available Computer-aided detection(CAD system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and cost-sensitive support vector machine(C-SVM classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO nodules(part solid and nonsolid. This method consists of several steps. Firstly, segmentation of regions of interest(ROIs, including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed, they are: the lung parenchyma is extracted based on threshold and morphologic segmentation method; the image denoising and enhancing is realized by nonlinear anisotropic diffusion filtering(NADF method; candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Then, lung nodules are classified by using the intelligent classifiers combining rules and C-SVM. Rule-based classification is first used to remove easily dismissible nonnodule objects, then C-SVM classification are used to further classify nodule candidates and reduce the number of false positive(FP objects. In order to increase the efficiency of SVM, an improved training method is used to train SVM, which uses the grid search method to search the optimal

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

Directory of Open Access Journals (Sweden)

Bin Li

2011-10-01

Full Text Available Computer-aided detection(CAD system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and costsensitive support vector machine(C-SVM classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO nodules(part solid and nonsolid. This method consists of several steps. Firstly, segmentation of regions of interest(ROIs, including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed, they are: the lung parenchyma is extracted based on threshold and morphologic segmentation method; the image denoising and enhancing is realized by nonlinear anisotropic diffusion filtering(NADF method;candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Then, lung nodules are classified by using the intelligent classifiers combining rules and C-SVM. Rule-based classification is first used to remove easily dismissible nonnodule objects, then C-SVM classification are used to further classify nodule candidates and reduce the number of false positive(FP objects. In order to increase the efficiency of SVM, an improved training method is used to train SVM, which uses the grid search method to search the optimal parameters

A spectral blanking-out controller for demonstration of information barrier technology

International Nuclear Information System (INIS)

Liu Suping; Gong Jian; Hu Guangchun; Zhang Jianhua

2006-01-01

Information barrier technology has become more and more important in the R and D of radiation fingerprint verification associated with classified items such as nuclear warheads, nuclear components and military-used nuclear materials. The function of information barriers is two-fold: one is to prevent the classified information from leaking out; the other is to provide creditable verification. To fulfill these two functions, the information barriers for a viable verification system (including all its hardware and software) must be designed on the basic principles of protecting classified information and the ability to authenticate. The Spectral Blanking-out Controller (SBC) is developed to illustrate the two functions of the information barriers and to explore some practice measures to meet the required design fundamentals. This paper briefs the task assigned to the SBC, the specific design concerns and the practical information barrier measures. The R and D of the SBC embodies the concepts of information barrier technology and has to conform to the basic guidelines: If a verification system is expected to possess strict information barriers, the design of the system must be integrative with due considerations given to the factors such as the efficiency of the verification technique, the possible measures to protect the classified information from directly or indirectly leaking out, the complete openness in all aspects of the system for the inspectors to authenticate the system for the sake of achieving certain degree of confidence on the verification results. (authors)

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

OpenAIRE

M. Favorskaya; A. Nosov; A. Popov

2015-01-01

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin dete...

Combined Approach of PNN and Time-Frequency as the Classifier for Power System Transient Problems

Directory of Open Access Journals (Sweden)

Aslam Pervez Memon

2013-04-01

Full Text Available The transients in power system cause serious disturbances in the reliability, safety and economy of the system. The transient signals possess the nonstationary characteristics in which the frequency as well as varying time information is compulsory for the analysis. Hence, it is vital, first to detect and classify the type of transient fault and then to mitigate them. This article proposes time-frequency and FFNN (Feedforward Neural Network approach for the classification of power system transients problems. In this work it is suggested that all the major categories of transients are simulated, de-noised, and decomposed with DWT (Discrete Wavelet and MRA (Multiresolution Analysis algorithm and then distinctive features are extracted to get optimal vector as input for training of PNN (Probabilistic Neural Network classifier. The simulation results of proposed approach prove their simplicity, accurateness and effectiveness for the automatic detection and classification of PST (Power System Transient types

78 FR 48076 - Facility Security Clearance and Safeguarding of National Security Information and Restricted Data

Science.gov (United States)

2013-08-07

...-2011-0268] RIN 3150-AJ07 Facility Security Clearance and Safeguarding of National Security Information..., Classified National Security Information. The rule would allow licensees flexibility in determining the means... licensee security education and training programs and enhances the protection of classified information...

COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

Directory of Open Access Journals (Sweden)

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.

Investor relations and information asymmetry

Directory of Open Access Journals (Sweden)

Sandrielem da Silva Rodrigues

2017-03-01

Full Text Available ABSTRACT Companies invest significant volumes of resources in investor relations (IR departments. The professionals working in the IR department are responsible for communication between the company and the market, so that the information generated is widely disseminated and understood by investors. In this context, this research aims to investigate whether there is evidence that the IR activity decreases information asymmetry between the company and the market. Specifically, we evaluate the hypothesis that Brazilian companies with IR websites classified as more informative have a reduced bid-ask spread (proxy for asymmetry. Therefore, this paper classifies the informative content from IR websites of Brazilian companies for the years 2013 and 2014 and relates the outcomes obtained with information asymmetry metrics. Initially, the estimation considers the pooled ordinary least squares (POLS model and, at a second moment, in order to mitigate potential endogeneity problems, the pooled two-stage least squares (2SLS model is used. The results indicate that more informative IR websites are able to decrease the bid-ask spread of Brazilian listed companies. This finding strongly encourages companies to provide information to stakeholders on well-structured IR websites of their own.

3 CFR 13526 - Executive Order 13526 of December 29, 2009. Classified National Security Information

Science.gov (United States)

2010-01-01

..., or protection services relating to the national security; or (h) the development, production, or use... small portion of an otherwise unclassified document or prepare a product to allow for dissemination at... information. Sec. 1.8. Classification Challenges. (a) Authorized holders of information who, in good faith...

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

Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search

NARCIS (Netherlands)

Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.

2001-01-01

In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search.

Snoring classified: The Munich-Passau Snore Sound Corpus.

Science.gov (United States)

Janott, Christoph; Schmitt, Maximilian; Zhang, Yue; Qian, Kun; Pandit, Vedhas; Zhang, Zixing; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Hemmert, Werner; Schuller, Björn

2018-03-01

Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Network Intrusion Detection System (NIDS in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier

Directory of Open Access Journals (Sweden)

Hafza A. Mahmood

2018-04-01

Full Text Available Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB Classifier to handle DoS attacks which is a data mining (DM model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB, proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.

General and Local: Averaged k-Dependence Bayesian Classifiers

Directory of Open Access Journals (Sweden)

Limin Wang

2015-06-01

Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.

Entropy based classifier for cross-domain opinion mining

Directory of Open Access Journals (Sweden)

Jyoti S. Deshmukh

2018-01-01

Full Text Available In recent years, the growth of social network has increased the interest of people in analyzing reviews and opinions for products before they buy them. Consequently, this has given rise to the domain adaptation as a prominent area of research in sentiment analysis. A classifier trained from one domain often gives poor results on data from another domain. Expression of sentiment is different in every domain. The labeling cost of each domain separately is very high as well as time consuming. Therefore, this study has proposed an approach that extracts and classifies opinion words from one domain called source domain and predicts opinion words of another domain called target domain using a semi-supervised approach, which combines modified maximum entropy and bipartite graph clustering. A comparison of opinion classification on reviews on four different product domains is presented. The results demonstrate that the proposed method performs relatively well in comparison to the other methods. Comparison of SentiWordNet of domain-specific and domain-independent words reveals that on an average 72.6% and 88.4% words, respectively, are correctly classified.

A Gene Expression Classifier of Node-Positive Colorectal Cancer

Directory of Open Access Journals (Sweden)

Paul F. Meeh

2009-10-01

Full Text Available We used digital long serial analysis of gene expression to discover gene expression differences between node-negative and node-positive colorectal tumors and developed a multigene classifier able to discriminate between these two tumor types. We prepared and sequenced long serial analysis of gene expression libraries from one node-negative and one node-positive colorectal tumor, sequenced to a depth of 26,060 unique tags, and identified 262 tags significantly differentially expressed between these two tumors (P < 2 x 10-6. We confirmed the tag-to-gene assignments and differential expression of 31 genes by quantitative real-time polymerase chain reaction, 12 of which were elevated in the node-positive tumor. We analyzed the expression levels of these 12 upregulated genes in a validation panel of 23 additional tumors and developed an optimized seven-gene logistic regression classifier. The classifier discriminated between node-negative and node-positive tumors with 86% sensitivity and 80% specificity. Receiver operating characteristic analysis of the classifier revealed an area under the curve of 0.86. Experimental manipulation of the function of one classification gene, Fibronectin, caused profound effects on invasion and migration of colorectal cancer cells in vitro. These results suggest that the development of node-positive colorectal cancer occurs in part through elevated epithelial FN1 expression and suggest novel strategies for the diagnosis and treatment of advanced disease.

Localizing genes to cerebellar layers by classifying ISH images.

Directory of Open Access Journals (Sweden)

Lior Kirsch

Full Text Available Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum. We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers, and use these patterns to predict localization for new genes. We analyze images of in-situ hybridization (ISH experiments, which we represent using histograms of local binary patterns (LBP and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje, granular, molecular and white matter layer. On held-out data, the layer classifiers achieve accuracy above 94% (AUC by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision. When applied to the full mouse genome, the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers. Many genes localized to the Purkinje layer are likely to be expressed in astrocytes, and many others are involved in lipid metabolism, possibly due to the unusual size of Purkinje cells.

Arabic Handwriting Recognition Using Neural Network Classifier

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier.

Science.gov (United States)

Mao, Keming; Deng, Zhuofu

2016-01-01

This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP) is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier

Directory of Open Access Journals (Sweden)

Keming Mao

2016-01-01

Full Text Available This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

Detection of microaneurysms in retinal images using an ensemble classifier

Directory of Open Access Journals (Sweden)

M.M. Habib

2017-01-01

Full Text Available This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature. A new set of 32 MA groundtruth images (with a total of 256 labelled MAs based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1 and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur.

Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

Science.gov (United States)

Abe, S

1998-01-01

In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables.

A Bayesian Classifier for X-Ray Pulsars Recognition

Directory of Open Access Journals (Sweden)

Hao Liang

2016-01-01

Full Text Available Recognition for X-ray pulsars is important for the problem of spacecraft’s attitude determination by X-ray Pulsar Navigation (XPNAV. By using the nonhomogeneous Poisson model of the received photons and the minimum recognition error criterion, a classifier based on the Bayesian theorem is proposed. For X-ray pulsars recognition with unknown Doppler frequency and initial phase, the features of every X-ray pulsar are extracted and the unknown parameters are estimated using the Maximum Likelihood (ML method. Besides that, a method to recognize unknown X-ray pulsars or X-ray disturbances is proposed. Simulation results certificate the validity of the proposed Bayesian classifier.

Adaptation in P300 braincomputer interfaces: A two-classifier cotraining approach

DEFF Research Database (Denmark)

Panicker, Rajesh C.; Sun, Ying; Puthusserypady, Sadasivan

2010-01-01

A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based braincomputer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fishers linear discriminant analysis and Bayesian linear discriminant analysis progressively...

Classified one-step high-radix signed-digit arithmetic units

Science.gov (United States)

Cherri, Abdallah K.

1998-08-01

High-radix number systems enable higher information storage density, less complexity, fewer system components, and fewer cascaded gates and operations. A simple one-step fully parallel high-radix signed-digit arithmetic is proposed for parallel optical computing based on new joint spatial encodings. This reduces hardware requirements and improves throughput by reducing the space-bandwidth produce needed. The high-radix signed-digit arithmetic operations are based on classifying the neighboring input digit pairs into various groups to reduce the computation rules. A new joint spatial encoding technique is developed to present both the operands and the computation rules. This technique increases the spatial bandwidth product of the spatial light modulators of the system. An optical implementation of the proposed high-radix signed-digit arithmetic operations is also presented. It is shown that our one-step trinary signed-digit and quaternary signed-digit arithmetic units are much simpler and better than all previously reported high-radix signed-digit techniques.

Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis

CSIR Research Space (South Africa)

Van Dyk, E

2008-11-01

Full Text Available classifier with Gaussian features while using any quadratic decision boundary. Therefore, the analysis is not restricted to Naive Bayesian classifiers alone and can, for instance, be used to calculate the Bayes error performance. We compare the analytical...

Automating the construction of scene classifiers for content-based video retrieval

NARCIS (Netherlands)

Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

2004-01-01

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

A unified classifier for robust face recognition based on combining multiple subspace algorithms

Science.gov (United States)

Ijaz Bajwa, Usama; Ahmad Taj, Imtiaz; Waqas Anwar, Muhammad

2012-10-01

Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.

A Review of Organizational Structures of Personal Information Management

OpenAIRE

Indratmo, J; Vassileva, Julita

2008-01-01

Personal information management (PIM) covers a large area of research fragmented into separate sub-areas such as file management, web bookmark organization, and email management. Consequently, it is hard to obtain a unified view of the various approaches to PIM developed in these different sub-areas. In this article, we synthesize and classify existing research on PIM based on the approach used to organize information items. We classify the organizational structures into five categories: hier...

Decoding Patent Information Using Patent Maps

OpenAIRE

Liu, Chen-Yuan; Yang, James Chingyu

2008-01-01

Patent information is a derivative product from the legal patent system. This information, which includes patent applications, patent descriptions, patent gazettes, patent abstracts, and patent data, is prepared in exact compliance with the regulations and specifications of the patent acts. Patent information, different from other published circulating information, is legally well protected. For convenience, this study classifies patent information into bibliographic and numeric data to creat...

Optimal threshold estimation for binary classifiers using game theory.

Science.gov (United States)

Sanchez, Ignacio Enrique

2016-01-01

Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic ( ROC ) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of "specificity equals sensitivity" maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

Directory of Open Access Journals (Sweden)

Boulesteix Anne-Laure

2009-12-01

Full Text Available Abstract Background In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data, since such analyses are particularly exposed to this kind of bias. Methods In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. Results We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case and the bias resulting from the choice of the classification method are examined both separately and jointly. Conclusions The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

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.

Gene-expression Classifier in Papillary Thyroid Carcinoma

DEFF Research Database (Denmark)

Londero, Stefano Christian; Jespersen, Marie Louise; Krogdahl, Annelise

2016-01-01

BACKGROUND: No reliable biomarker for metastatic potential in the risk stratification of papillary thyroid carcinoma exists. We aimed to develop a gene-expression classifier for metastatic potential. MATERIALS AND METHODS: Genome-wide expression analyses were used. Development cohort: freshly...

A History of Classified Activities at Oak Ridge National Laboratory

Energy Technology Data Exchange (ETDEWEB)

Quist, A.S.

2001-01-30

The facilities that became Oak Ridge National Laboratory (ORNL) were created in 1943 during the United States' super-secret World War II project to construct an atomic bomb (the Manhattan Project). During World War II and for several years thereafter, essentially all ORNL activities were classified. Now, in 2000, essentially all ORNL activities are unclassified. The major purpose of this report is to provide a brief history of ORNL's major classified activities from 1943 until the present (September 2000). This report is expected to be useful to the ORNL Classification Officer and to ORNL's Authorized Derivative Classifiers and Authorized Derivative Declassifiers in their classification review of ORNL documents, especially those documents that date from the 1940s and 1950s.

Joint two-view information for computerized detection of microcalcifications on mammograms

International Nuclear Information System (INIS)

Sahiner, Berkman; Chan, H.-P.; Hadjiiski, Lubomir M.; Helvie, Mark A.; Paramagul, Chinatana; Ge Jun; Wei Jun; Zhou Chuan

2006-01-01

We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Candidate pairs were classified with a similarity classifier that used the joint information from both views. Each cluster candidate was also characterized by its single-view features. The outputs of the similarity classifier and the single-view classifier were fused and the cluster candidate was classified as a true microcalcification cluster or a false-positive (FP) using the fused two-view information. A data set of 116 pairs of mammograms containing microcalcification clusters and 203 pairs of normal images from the University of South Florida (USF) public database was used for training the two-view detection algorithm. The trained method was tested on an independent test set of 167 pairs of mammograms, which contained 71 normal pairs and 96 pairs with microcalcification clusters collected at the University of Michigan (UM). The similarity classifier had a very low FP rate for the test set at low and medium levels of sensitivity. However, the highest mammogram-based sensitivity that could be reached by the similarity classifier was 69%. The single-view classifier had a higher FP rate compared to the similarity classifier, but it could reach a maximum mammogram-based sensitivity of 93%. The fusion method combined the scores of these two classifiers so that the number of FPs was substantially reduced at relatively low and medium sensitivities, and a relatively high maximum sensitivity was maintained. For the malignant microcalcification clusters, at a mammogram-based sensitivity of 80%, the FP rates were 0.18 and 0.35 with the two-view fusion and single-view detection methods, respectively. When the

Oblique decision trees using embedded support vector machines in classifier ensembles

NARCIS (Netherlands)

Menkovski, V.; Christou, I.; Efremidis, S.

2008-01-01

Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

A naïve Bayes classifier for planning transfusion requirements in heart surgery.

Science.gov (United States)

Cevenini, Gabriele; Barbini, Emanuela; Massai, Maria R; Barbini, Paolo

2013-02-01

Transfusion of allogeneic blood products is a key issue in cardiac surgery. Although blood conservation and standard transfusion guidelines have been published by different medical groups, actual transfusion practices after cardiac surgery vary widely among institutions. Models can be a useful support for decision making and may reduce the total cost of care. The objective of this study was to propose and evaluate a procedure to develop a simple locally customized decision-support system. We analysed 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. Univariate statistical tests were performed to identify a set of preoperative and intraoperative variables as likely independent features for planning transfusion quantities. These features were utilized to design a naïve Bayes classifier. Model performance was evaluated using the leave-one-out cross-validation approach. All computations were done using spss and matlab code. The overall correct classification percentage was not particularly high if several classes of patients were to be identified. Model performance improved appreciably when the patient sample was divided into two classes (transfused and non-transfused patients). In this case the naïve Bayes model correctly classified about three quarters of patients with 71.2% sensitivity and 78.4% specificity, thus providing useful information for recognizing patients with transfusion requirements in the specific scenario considered. Although the classifier is customized to a particular setting and cannot be generalized to other scenarios, the simplicity of its development and the results obtained make it a promising approach for designing a simple model for different heart surgery centres needing a customized decision-support system for planning transfusion requirements in intensive care unit. © 2011 Blackwell Publishing Ltd.

29 CFR 1926.407 - Hazardous (classified) locations.

Science.gov (United States)

2010-07-01

...) locations, unless modified by provisions of this section. (b) Electrical installations. Equipment, wiring..., DEPARTMENT OF LABOR (CONTINUED) SAFETY AND HEALTH REGULATIONS FOR CONSTRUCTION Electrical Installation Safety... electric equipment and wiring in locations which are classified depending on the properties of the...

Integrating the Supervised Information into Unsupervised Learning

Directory of Open Access Journals (Sweden)

Ping Ling

2013-01-01

Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

Theory of the Concealed Information Test

NARCIS (Netherlands)

Verschuere, B.; Ben-Shakhar, G.; Verschuere, B.; Ben-Shakhar, G.; Meijer, E.

2011-01-01

It is now well established that physiological measures can be validly used to detect concealed information. An important challenge is to elucidate the underlying mechanisms of concealed information detection. We review theoretical approaches that can be broadly classified in two major categories:

15 CFR 705.6 - Confidential information.

Science.gov (United States)

2010-01-01

... EFFECT OF IMPORTED ARTICLES ON THE NATIONAL SECURITY § 705.6 Confidential information. (a) Any... the investigation that would disclose national security classified information or business... Security Classified” or “Business Confidential,” as appropriate, marked at the top of each sheet. Any...

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

Science.gov (United States)

Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita

2018-01-01

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

78 FR 69286 - Facility Security Clearance and Safeguarding of National Security Information and Restricted Data

Science.gov (United States)

2013-11-19

... Clearance and Safeguarding of National Security Information and Restricted Data AGENCY: Nuclear Regulatory... Executive Order 13526, Classified National Security Information. In addition, this direct final rule allowed... licensees (or their designees) to conduct classified [[Page 69287

Scoring and Classifying Examinees Using Measurement Decision Theory

Directory of Open Access Journals (Sweden)

Lawrence M. Rudner

2009-04-01

Full Text Available This paper describes and evaluates the use of measurement decision theory (MDT to classify examinees based on their item response patterns. The model has a simple framework that starts with the conditional probabilities of examinees in each category or mastery state responding correctly to each item. The presented evaluation investigates: (1 the classification accuracy of tests scored using decision theory; (2 the effectiveness of different sequential testing procedures; and (3 the number of items needed to make a classification. A large percentage of examinees can be classified accurately with very few items using decision theory. A Java Applet for self instruction and software for generating, calibrating and scoring MDT data are provided.

Classifier utility modeling and analysis of hypersonic inlet start/unstart considering training data costs

Science.gov (United States)

Chang, Juntao; Hu, Qinghua; Yu, Daren; Bao, Wen

2011-11-01

Start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection control of scramjet. The inlet start/unstart detection can be attributed to a standard pattern classification problem, and the training sample costs have to be considered for the classifier modeling as the CFD numerical simulations and wind tunnel experiments of hypersonic inlets both cost time and money. To solve this problem, the CFD simulation of inlet is studied at first step, and the simulation results could provide the training data for pattern classification of hypersonic inlet start/unstart. Then the classifier modeling technology and maximum classifier utility theories are introduced to analyze the effect of training data cost on classifier utility. In conclusion, it is useful to introduce support vector machine algorithms to acquire the classifier model of hypersonic inlet start/unstart, and the minimum total cost of hypersonic inlet start/unstart classifier can be obtained by the maximum classifier utility theories.

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

NARCIS (Netherlands)

Drugan, Madalina M.; Wiering, Marco A.

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

Detection of Fundus Lesions Using Classifier Selection

Science.gov (United States)

Nagayoshi, Hiroto; Hiramatsu, Yoshitaka; Sako, Hiroshi; Himaga, Mitsutoshi; Kato, Satoshi

A system for detecting fundus lesions caused by diabetic retinopathy from fundus images is being developed. The system can screen the images in advance in order to reduce the inspection workload on doctors. One of the difficulties that must be addressed in completing this system is how to remove false positives (which tend to arise near blood vessels) without decreasing the detection rate of lesions in other areas. To overcome this difficulty, we developed classifier selection according to the position of a candidate lesion, and we introduced new features that can distinguish true lesions from false positives. A system incorporating classifier selection and these new features was tested in experiments using 55 fundus images with some lesions and 223 images without lesions. The results of the experiments confirm the effectiveness of the proposed system, namely, degrees of sensitivity and specificity of 98% and 81%, respectively.

Classifier-Guided Sampling for Complex Energy System Optimization

Energy Technology Data Exchange (ETDEWEB)

Backlund, Peter B. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

2015-09-01

This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of o bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.

Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992

Science.gov (United States)

Qi, Sharon L.; Konduris, Alexandria; Litke, David W.; Dupree, Jean

2002-01-01

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).

Empirical study of classification process for two-stage turbo air classifier in series

Science.gov (United States)

Yu, Yuan; Liu, Jiaxiang; Li, Gang

2013-05-01

The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the "fish-hook" effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the "fish-hook" effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.

Case base classification on digital mammograms: improving the performance of case base classifier

Science.gov (United States)

Raman, Valliappan; Then, H. H.; Sumari, Putra; Venkatesa Mohan, N.

2011-10-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

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.

Variants of the Borda count method for combining ranked classifier hypotheses

NARCIS (Netherlands)

van Erp, Merijn; Schomaker, Lambert; Schomaker, Lambert; Vuurpijl, Louis

2000-01-01

The Borda count is a simple yet effective method of combining rankings. In pattern recognition, classifiers are often able to return a ranked set of results. Several experiments have been conducted to test the ability of the Borda count and two variant methods to combine these ranked classifier

Should OCD be classified as an anxiety disorder in DSM-V?

NARCIS (Netherlands)

Stein, Dan J.; Fineberg, Naomi A.; Bienvenu, O. Joseph; Denys, Damiaan; Lochner, Christine; Nestadt, Gerald; Leckman, James F.; Rauch, Scott L.; Phillips, Katharine A.

2010-01-01

In DSM-III, DSM-III-R, and DSM-IV, obsessive-compulsive disorder (OCD) was classified as an anxiety disorder. In ICD-10, OCD is classified separately from the anxiety disorders, although within the same larger category as anxiety disorders (as one of the "neurotic, stress-related, and somatoform

29 CFR 1910.307 - Hazardous (classified) locations.

Science.gov (United States)

2010-07-01

... equipment at the location. (c) Electrical installations. Equipment, wiring methods, and installations of... covers the requirements for electric equipment and wiring in locations that are classified depending on... provisions of this section. (4) Division and zone classification. In Class I locations, an installation must...

Scientometric Indicators as a Way to Classify Brands for Customer’s Information

Directory of Open Access Journals (Sweden)

Mihaela Paun

2015-10-01

Full Text Available The paper proposes a novel approach for classification of different brands that commercialize similar products, for customer information. The approach is tested on electronic shopping records found on Amazon.com, by quantifying customer behavior and comparing the results with classifications of the same brands found online through search engines. The indicators proposed for the classification are currently used scientometric measures that can be easily applied to marketing classification.

An SVM classifier to separate false signals from microcalcifications in digital mammograms

Energy Technology Data Exchange (ETDEWEB)

Bazzani, Armando; Bollini, Dante; Brancaccio, Rosa; Campanini, Renato; Riccardi, Alessandro; Romani, Davide [Department of Physics, University of Bologna (Italy); INFN, Bologna (Italy); Lanconelli, Nico [Department of Physics, University of Bologna, and INFN, Bologna (Italy). E-mail: nico.lanconelli@bo.infn.it; Bevilacqua, Alessandro [Department of Electronics, Computer Science and Systems, University of Bologna, and INFN, Bologna (Italy)

2001-06-01

In this paper we investigate the feasibility of using an SVM (support vector machine) classifier in our automatic system for the detection of clustered microcalcifications in digital mammograms. SVM is a technique for pattern recognition which relies on the statistical learning theory. It minimizes a function of two terms: the number of misclassified vectors of the training set and a term regarding the generalization classifier capability. We compare the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of our detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features. The SVM classifier gets slightly better results than the MLP one (Az value of 0.963 against 0.958) in the presence of a high number of training data; the improvement becomes much more evident (Az value of 0.952 against 0.918) in training sets of reduced size. Finally, the setting of the SVM classifier is much easier than the MLP one. (author)

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

Science.gov (United States)

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

Learning for VMM + WTA Embedded Classifiers

Science.gov (United States)

2016-03-31

Learning for VMM + WTA Embedded Classifiers Jennifer Hasler and Sahil Shah Electrical and Computer Engineering Georgia Institute of Technology...enabling correct classification of each novel acoustic signal (generator, idle car, and idle truck ). The classification structure requires, after...measured on our SoC FPAA IC. The test input is composed of signals from urban environment for 3 objects (generator, idle car, and idle truck

Automated Classification of Consumer Health Information Needs in Patient Portal Messages.

Science.gov (United States)

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

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804-0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs.

Automatic Human Facial Expression Recognition Based on Integrated Classifier From Monocular Video with Uncalibrated Camera

Directory of Open Access Journals (Sweden)

Yu Tao

2017-01-01

Full Text Available An automatic recognition framework for human facial expressions from a monocular video with an uncalibrated camera is proposed. The expression characteristics are first acquired from a kind of deformable template, similar to a facial muscle distribution. After associated regularization, the time sequences from the trait changes in space-time under complete expressional production are then arranged line by line in a matrix. Next, the matrix dimensionality is reduced by a method of manifold learning of neighborhood-preserving embedding. Finally, the refined matrix containing the expression trait information is recognized by a classifier that integrates the hidden conditional random field (HCRF and support vector machine (SVM. In an experiment using the Cohn–Kanade database, the proposed method showed a comparatively higher recognition rate than the individual HCRF or SVM methods in direct recognition from two-dimensional human face traits. Moreover, the proposed method was shown to be more robust than the typical Kotsia method because the former contains more structural characteristics of the data to be classified in space-time

The three-dimensional origin of the classifying algebra

International Nuclear Information System (INIS)

Fuchs, Juergen; Schweigert, Christoph; Stigner, Carl

2010-01-01

It is known that reflection coefficients for bulk fields of a rational conformal field theory in the presence of an elementary boundary condition can be obtained as representation matrices of irreducible representations of the classifying algebra, a semisimple commutative associative complex algebra. We show how this algebra arises naturally from the three-dimensional geometry of factorization of correlators of bulk fields on the disk. This allows us to derive explicit expressions for the structure constants of the classifying algebra as invariants of ribbon graphs in the three-manifold S 2 xS 1 . Our result unravels a precise relation between intertwiners of the action of the mapping class group on spaces of conformal blocks and boundary conditions in rational conformal field theories.

Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection

Science.gov (United States)

Safi’ie, M. A.; Utami, E.; Fatta, H. A.

2018-03-01

Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.

Protection of National Security Information

National Research Council Canada - National Science Library

Elsea, Jennifer K

2006-01-01

Recent cases involving alleged disclosures of classified information to the news media or others who are not entitled to receive it have renewed Congress s interest with regard to the possible need...

78 FR 5116 - NASA Information Security Protection

Science.gov (United States)

2013-01-24

... 2700-AD61 NASA Information Security Protection AGENCY: National Aeronautics and Space Administration..., projects, plans, or protection services relating to the national security; or (h) The development... implement the provisions of Executive Order (E.O.) 13526, Classified National Security Information, and...

A new entropy function for feature extraction with the refined scores as a classifier for the unconstrained ear verification

Directory of Open Access Journals (Sweden)

Mamta Bansal

2017-05-01

Full Text Available For high end security like surveillance there is a need for a robust system capable of verifying a person under the unconstrained conditions. This paper presents the ear based verification system using a new entropy function that changes not only the information gain function but also the information source values. This entropy function displays peculiar characteristics such as splitting into two modes. Two types of entropy features: Effective Gaussian Information source value and Effective Exponential Information source value functions are derived using the entropy function. To classify the entropy features we have devised refined scores (RS method that refines the scores generated using the Euclidean distance. The experimental results vindicate the superiority of proposed method over literature.

A Novel Cascade Classifier for Automatic Microcalcification Detection.

Directory of Open Access Journals (Sweden)

Seung Yeon Shin

Full Text Available In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC. Our framework comprises three classification stages: i a random forest (RF classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii a more complex discriminative restricted Boltzmann machine (DRBM classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC curve for detection of clustered μCs.

Feature weighting using particle swarm optimization for learning vector quantization classifier

Science.gov (United States)

Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

Directory of Open Access Journals (Sweden)

M. Njah

2017-06-01

Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

40 CFR 260.32 - Variances to be classified as a boiler.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 25 2010-07-01 2010-07-01 false Variances to be classified as a boiler... be classified as a boiler. In accordance with the standards and criteria in § 260.10 (definition of “boiler”), and the procedures in § 260.33, the Administrator may determine on a case-by-case basis that...

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Science.gov (United States)

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection

How large a training set is needed to develop a classifier for microarray data?

Science.gov (United States)

Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

2008-01-01

A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Automated Classification of Consumer Health Information Needs in Patient Portal Messages

Science.gov (United States)

Cronin, Robert M.; Fabbri, Daniel; Denny, Joshua C.; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804–0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs. PMID:26958285

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

Directory of Open Access Journals (Sweden)

Chee Keong Kwoh

2011-04-01

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

Proposed hybrid-classifier ensemble algorithm to map snow cover area

Science.gov (United States)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS

OpenAIRE

Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita

2016-01-01

This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1

Measurement properties of clinical assessment methods for classifying generalized joint hypermobility

DEFF Research Database (Denmark)

Juul-Kristensen, Birgit; Schmedling, Karoline; Rombaut, Lies

2017-01-01

methods. For BS-self, the validity showed unknown evidence compared with test assessment methods. In conclusion, following recommended uniformity of testing procedures, the recommendation for clinical use in adults is BS with cut-point of 5 of 9 including historical information, while in children it is BS...... with cut-point of at least 6 of 9. However, more studies are needed to conclude on the validity properties of these assessment methods, and before evidence-based recommendations can be made for clinical use on the "best" assessment method for classifying GJH. © 2017 Wiley Periodicals, Inc....... for evaluating the methodological quality of the identified studies, all included studies were rated "fair" or "poor." Most studies were using BS, and for BS the reliability most of the studies showed limited positive to conflicting evidence, with some shortcomings on studies for the validity. The three other...

Two-categorical bundles and their classifying spaces

DEFF Research Database (Denmark)

Baas, Nils A.; Bökstedt, M.; Kro, T.A.

2012-01-01

-category is a classifying space for the associated principal 2-bundles. In the process of proving this we develop a lot of powerful machinery which may be useful in further studies of 2-categorical topology. As a corollary we get a new proof of the classification of principal bundles. A calculation based...

Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China

Science.gov (United States)

Wang, Yan-ying; Wang, Jin-liang; Wang, Ping; Hu, Wen-yin; Su, Shao-hua

2015-12-01

High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

A Bayesian classifier for symbol recognition

OpenAIRE

Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

2007-01-01

URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

Air classifier technology (ACT) in dry powder inhalation. Part 1 : Introduction of a novel force distribution concept (FDC) explaining the performance of a basic air classifier on adhesive mixtures

NARCIS (Netherlands)

de Boer, A H; Hagedoorn, P; Gjaltema, D; Goede, J; Frijlink, H W

2003-01-01

Air classifier technology (ACT) is introduced as part of formulation integrated dry powder inhaler development (FIDPI) to optimise the de-agglomeration of inhalation powders. Carrier retention and de-agglomeration results obtained with a basic classifier concept are discussed. The theoretical

Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis

International Nuclear Information System (INIS)

Li Qiang; Doi Kunio

2006-01-01

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an 'optimal' method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect

A Naive-Bayes classifier for damage detection in engineering materials

Energy Technology Data Exchange (ETDEWEB)

Addin, O. [Laboratory of Intelligent Systems, Institute of Advanced Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia); Sapuan, S.M. [Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia)]. E-mail: sapuan@eng.upm.edu.my; Mahdi, E. [Department of Aerospace Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia); Othman, M. [Department of Communication Technology and Networks, Universiti Putra Malaysia, 43400 Serdang, Selangor (Malaysia)

2007-07-01

This paper is intended to introduce the Bayesian network in general and the Naive-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naive-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. The data sets were conducted based on artificial damages created in quasi isotopic laminated composites of the AS4/3501-6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naive-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials.

Discovering mammography-based machine learning classifiers for breast cancer diagnosis.

Science.gov (United States)

Ramos-Pollán, Raúl; Guevara-López, Miguel Angel; Suárez-Ortega, Cesar; Díaz-Herrero, Guillermo; Franco-Valiente, Jose Miguel; Rubio-Del-Solar, Manuel; González-de-Posada, Naimy; Vaz, Mario Augusto Pires; Loureiro, Joana; Ramos, Isabel

2012-08-01

This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.

A Topic Model Approach to Representing and Classifying Football Plays

KAUST Repository

Varadarajan, Jagannadan

2013-09-09

We address the problem of modeling and classifying American Football offense teams’ plays in video, a challenging example of group activity analysis. Automatic play classification will allow coaches to infer patterns and tendencies of opponents more ef- ficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. To this end, we develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximiza- tion of both likelihood and inter-class margins of MedLDA in learning the topics allows us to learn semantically meaningful play type templates, as well as, classify different play types with 70% average accuracy. Furthermore, this method is extended to analyze individual player roles in classifying each play type. We validate our method on a large dataset comprising 271 play clips from real-world football games, which will be made publicly available for future comparisons.

Central index of dose information

International Nuclear Information System (INIS)

1991-01-01

The Central Index of Dose Information (CIDI) is a national database of occupational exposure to radiation operated by the NRPB as agent for the Health and Safety Executive. It receives summarised information on the radiation doses to classified persons in Great Britain annually from Approved Dosimetry Services. This document is the first annual CIDI summary of the data, giving statistics for 1986. (UK)

Histogram deconvolution - An aid to automated classifiers

Science.gov (United States)

Lorre, J. J.

1983-01-01

It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.

Characterizing, Classifying, and Understanding Information Security Laws and Regulations: Considerations for Policymakers and Organizations Protecting Sensitive Information Assets

Science.gov (United States)

Thaw, David Bernard

2011-01-01

Current scholarly understanding of information security regulation in the United States is limited. Several competing mechanisms exist, many of which are untested in the courts and before state regulators, and new mechanisms are being proposed on a regular basis. Perhaps of even greater concern, the pace at which technology and threats change far…

Self-organizing map classifier for stressed speech recognition

Science.gov (United States)

Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

2016-05-01

This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

Science.gov (United States)

McIntyre, Alexa B R; Ounit, Rachid; Afshinnekoo, Ebrahim; Prill, Robert J; Hénaff, Elizabeth; Alexander, Noah; Minot, Samuel S; Danko, David; Foox, Jonathan; Ahsanuddin, Sofia; Tighe, Scott; Hasan, Nur A; Subramanian, Poorani; Moffat, Kelly; Levy, Shawn; Lonardi, Stefano; Greenfield, Nick; Colwell, Rita R; Rosen, Gail L; Mason, Christopher E

2017-09-21

One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Directory of Open Access Journals (Sweden)

Debesh Jha

2017-01-01

Full Text Available Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT for feature extraction, probabilistic principal component analysis (PPCA for dimensionality reduction, and a random subspace ensemble (RSE classifier along with the K-nearest neighbors (KNN algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV, the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.

Patentna zaštita poverljivih pronalazaka / Patent protection of classified invention

Directory of Open Access Journals (Sweden)

Obrad T. Čabarkapa

2008-10-01

Full Text Available Svaki pronalazak za koji se utvrdi da je značajan za odbranu i bezbednost Republike Srbije smatra se poverljivim. Za patentnu zaštitu poverljivih pronalazaka podnosi se prijava organu nadležnom za poslove odbrane, koji ima isključivo pravo da raspolaže poverljivim pronalascima1. U organizacijskoj jedinici nadležnoj za poslove naučne i inovacione delatnosti2 realizuje postupak ispitivanja poverljivih prijava patenata. Da bi se donela ocena o poverljivosti prijavljenog pronalaska neophodno je realizovati određene faze u postupku ispitivanja prijave. Poverljivi pronalazak se ne objavljuje, a pronalazač, nakon priznavanja patenta, u skladu sa zakonskim propisima, ima određena moralna i materijalna prava. / Every invention established to be of significance for defense or security of the Republic of Serbia is considered to be a classified invention. For the purpose of patent protection of classified inventions, a confidential application must be submitted to a relevant defense authority having the exclusive right to deal with classified inventions3. An organizational unit competent for scientific and innovation issues carries out the examination of classified patent applications. In order to evaluate classification of the submitted invention, regarding its significance for defense or security of the country as well as to make the final decision on the application, the examination procedure should be carried out through several phases. A classified invention is not to be published and once the patent has been approved, the inventor enjoys moral and material rights in accordance with law.

On minimal inhibitory rules for almost all k-valued information systems

KAUST Repository

Moshkov, Mikhail

2009-07-30

The minimal inhibitory rules for information systems can be used for construction of classifiers. We show that almost all information systems from a certain large class of information systems have relatively short minimal inhibitory rules. However, the number of such rules is not polynomial in the number of attributes and the number of objects. This class consists of all k-valued information systems, k ≥ 2, with the number of objects polynomial in the number of attributes. Hence, for efficient construction of classifiers some filtration techniques in rule generation are necessary. Another way is to work with lazy classification algorithms based on inhibitory rules.

Neural Network Classifier Based on Growing Hyperspheres

Czech Academy of Sciences Publication Activity Database

Jiřina Jr., Marcel; Jiřina, Marcel

2000-01-01

Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

Consensus of sample-balanced classifiers for identifying ligand-binding residue by co-evolutionary physicochemical characteristics of amino acids

KAUST Repository

Chen, Peng

2013-01-01

Protein-ligand binding is an important mechanism for some proteins to perform their functions, and those binding sites are the residues of proteins that physically bind to ligands. So far, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available. In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. Due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets, for each of which a random forest (RF)-based classifier was trained. The ensemble of these RF classifiers formed a sequence-based protein-ligand binding site predictor. Experimental results on CASP9 targets demonstrated that our method compared favorably with the state-of-the-art. © Springer-Verlag Berlin Heidelberg 2013.

Performance evaluation of various classifiers for color prediction of rice paddy plant leaf

Science.gov (United States)

Singh, Amandeep; Singh, Maninder Lal

2016-11-01

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.

Clean-up progress at the SNL/NM Classified Waste Landfill

International Nuclear Information System (INIS)

Slavin, P.J.; Galloway, R.B.

1999-01-01

The Sandia National Laboratories/New Mexico (SNL/NM)Environmental Restoration Project is currently excavating the Classified Waste Landfill in Technical Area II, a disposal area for weapon components for approximately 40 years until it closed in 1987. Many different types of classified parts were disposed in unlined trenches and pits throughout the course of the landfill's history. A percentage of the parts contain explosives and/or radioactive components or contamination. The excavation has progressed backward chronologically from the last trenches filled through to the earlier pits. Excavation commenced in March 1998, and approximately 75 percent of the site (as defined by geophysical anomalies) has been completed as of November 1999. The material excavated consists primarily of classified weapon assemblies and related components, so disposition must include demilitarization and sanitization. This has resulted in substantial waste minimization and cost avoidance for the project as upwards of 90 percent of the classified materials are being demilitarized and recycled. The project is using field screening and lab analysis in conjunction with preliminary and in-process risk assessments to characterize soil and make waste determinations in a timely a fashion as possible. Challenges in waste management have prompted the adoption of innovative solutions. The hand-picked crew (both management and field staff) and the ability to quickly adapt to changing conditions has ensured the success of the project. The current schedule is to complete excavation in July 2000, with follow-on verification sampling, demilitarization, and waste management activities following

Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier

Directory of Open Access Journals (Sweden)

Krishna Kant Singh

2017-06-01

Full Text Available A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

Science.gov (United States)

Liu, Tao; Chen, Lei; Pan, Xiaoyong

2018-05-31

Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

Use of artificial neural networks and geographic objects for classifying remote sensing imagery

Directory of Open Access Journals (Sweden)

Pedro Resende Silva

2014-06-01

Full Text Available The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1 to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2 to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3 to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features

Directory of Open Access Journals (Sweden)

Jaimit Parikh

2017-11-01

Full Text Available While pre-clinical Torsades de Pointes (TdP risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features. Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the in-silico models (derived features. The classifiers built on derived features have thus far not consistently provided increased prediction accuracies, and hence casts doubt on the value of such approaches given the cost of including biophysical detail. Here, we propose a new two-step method for TdP risk classification, referred to as Multi-Channel Blockage at Early After Depolarization (MCB@EAD. In the first step, we classified the compound that produced insufficient hERG block as non-torsadogenic. In the second step, the role of non-hERG channels to modulate TdP risk are considered by constructing classifiers based on direct or derived features at critical hERG block concentrations that generates EADs in the computational cardiac cell models. MCB@EAD provides comparable or superior TdP risk classification of the drugs from the direct features in tests against published methods. TdP risk for the drugs highly correlated to the propensity to generate EADs in the model. However, the derived features of the biophysical models did not improve the predictive capability for TdP risk assessment.

Classifying Coding DNA with Nucleotide Statistics

Directory of Open Access Journals (Sweden)

Nicolas Carels

2009-10-01

Full Text Available In this report, we compared the success rate of classification of coding sequences (CDS vs. introns by Codon Structure Factor (CSF and by a method that we called Universal Feature Method (UFM. UFM is based on the scoring of purine bias (Rrr and stop codon frequency. We show that the success rate of CDS/intron classification by UFM is higher than by CSF. UFM classifies ORFs as coding or non-coding through a score based on (i the stop codon distribution, (ii the product of purine probabilities in the three positions of nucleotide triplets, (iii the product of Cytosine (C, Guanine (G, and Adenine (A probabilities in the 1st, 2nd, and 3rd positions of triplets, respectively, (iv the probabilities of G in 1st and 2nd position of triplets and (v the distance of their GC3 vs. GC2 levels to the regression line of the universal correlation. More than 80% of CDSs (true positives of Homo sapiens (>250 bp, Drosophila melanogaster (>250 bp and Arabidopsis thaliana (>200 bp are successfully classified with a false positive rate lower or equal to 5%. The method releases coding sequences in their coding strand and coding frame, which allows their automatic translation into protein sequences with 95% confidence. The method is a natural consequence of the compositional bias of nucleotides in coding sequences.

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.

Mercury⊕: An evidential reasoning image classifier

Science.gov (United States)

Peddle, Derek R.

1995-12-01

MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.

Waste classifying and separation device

International Nuclear Information System (INIS)

Kakiuchi, Hiroki.

1997-01-01

A flexible plastic bags containing solid wastes of indefinite shape is broken and the wastes are classified. The bag cutting-portion of the device has an ultrasonic-type or a heater-type cutting means, and the cutting means moves in parallel with the transferring direction of the plastic bags. A classification portion separates and discriminates the plastic bag from the contents and conducts classification while rotating a classification table. Accordingly, the plastic bag containing solids of indefinite shape can be broken and classification can be conducted efficiently and reliably. The device of the present invention has a simple structure which requires small installation space and enables easy maintenance. (T.M.)

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

Science.gov (United States)

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. PMID:26543867

Tumor Microenvironment Gene Signature as a Prognostic Classifier and Therapeutic Target

Science.gov (United States)

2016-06-01

AWARD NUMBER: W81XWH-14-1-0107 TITLE: Tumor Microenvironment Gene Signature as a Prognostic Classifier and Therapeutic Target PRINCIPAL...AND SUBTITLE Tumor Microenvironment Gene Signature as a 5a. CONTRACT NUMBER W81XWH-14-1-0107 Prognostic Classifier and Therapeutic Target 5b...gene signature that correlates with poor survival in ovarian cancer patients. We are refining this gene signature to develop biomarkers for the

An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States

Science.gov (United States)

2015-12-22

AFRL-AFOSR-VA-TR-2016-0037 An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States Adrian Lee UNIVERSITY OF WASHINGTON...to 14-09-2015 4. TITLE AND SUBTITLE An Integrated Neuroscience and Engineering Approach to Classifying Human Brain- States 5a.  CONTRACT NUMBER 5b...specific cognitive states remains elusive, owing perhaps to limited crosstalk between the fields of neuroscience and engineering. Here, we report a

Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.

Science.gov (United States)

Cascianelli, Silvia; Scialpi, Michele; Amici, Serena; Forini, Nevio; Minestrini, Matteo; Fravolini, Mario Luca; Sinzinger, Helmut; Schillaci, Orazio; Palumbo, Barbara

2017-01-01

Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer's Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis - PCA - or Partial Least Squares - PLS - are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

Patients on weaning trials classified with support vector machines

International Nuclear Information System (INIS)

Garde, Ainara; Caminal, Pere; Giraldo, Beatriz F; Schroeder, Rico; Voss, Andreas; Benito, Salvador

2010-01-01

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h (GSucc); 39 patients who failed the weaning trial (GFail) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (GRein). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients (GSucc, GFail and GRein) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications

A support vector machine (SVM) based voltage stability classifier

Energy Technology Data Exchange (ETDEWEB)

Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)

2007-07-01

Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

Science.gov (United States)

Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

2012-12-01

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

Reconfigurable support vector machine classifier with approximate computing

NARCIS (Netherlands)

van Leussen, M.J.; Huisken, J.; Wang, L.; Jiao, H.; De Gyvez, J.P.

2017-01-01

Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and

Fusion of classifiers for REIS-based detection of suspicious breast lesions

Science.gov (United States)

Lederman, Dror; Wang, Xingwei; Zheng, Bin; Sumkin, Jules H.; Tublin, Mitchell; Gur, David

2011-03-01

After developing a multi-probe resonance-frequency electrical impedance spectroscopy (REIS) system aimed at detecting women with breast abnormalities that may indicate a developing breast cancer, we have been conducting a prospective clinical study to explore the feasibility of applying this REIS system to classify younger women (breast cancer. The system comprises one central probe placed in contact with the nipple, and six additional probes uniformly distributed along an outside circle to be placed in contact with six points on the outer breast skin surface. In this preliminary study, we selected an initial set of 174 examinations on participants that have completed REIS examinations and have clinical status verification. Among these, 66 examinations were recommended for biopsy due to findings of a highly suspicious breast lesion ("positives"), and 108 were determined as negative during imaging based procedures ("negatives"). A set of REIS-based features, extracted using a mirror-matched approach, was computed and fed into five machine learning classifiers. A genetic algorithm was used to select an optimal subset of features for each of the five classifiers. Three fusion rules, namely sum rule, weighted sum rule and weighted median rule, were used to combine the results of the classifiers. Performance evaluation was performed using a leave-one-case-out cross-validation method. The results indicated that REIS may provide a new technology to identify younger women with higher than average risk of having or developing breast cancer. Furthermore, it was shown that fusion rule, such as a weighted median fusion rule and a weighted sum fusion rule may improve performance as compared with the highest performing single classifier.

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

Science.gov (United States)

Ghosh, Samiran; Wang, Yazhen

2015-02-01

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

A decision support system using combined-classifier for high-speed data stream in smart grid

Science.gov (United States)

Yang, Hang; Li, Peng; He, Zhian; Guo, Xiaobin; Fong, Simon; Chen, Huajun

2016-11-01

Large volume of high-speed streaming data is generated by big power grids continuously. In order to detect and avoid power grid failure, decision support systems (DSSs) are commonly adopted in power grid enterprises. Among all the decision-making algorithms, incremental decision tree is the most widely used one. In this paper, we propose a combined classifier that is a composite of a cache-based classifier (CBC) and a main tree classifier (MTC). We integrate this classifier into a stream processing engine on top of the DSS such that high-speed steaming data can be transformed into operational intelligence efficiently. Experimental results show that our proposed classifier can return more accurate answers than other existing ones.

Fusing in vivo and ex vivo NMR sources of information for brain tumor classification

International Nuclear Information System (INIS)

Croitor-Sava, A R; Laudadio, T; Sima, D M; Van Huffel, S; Martinez-Bisbal, M C; Celda, B; Piquer, J; Heerschap, A

2011-01-01

In this study we classify short echo-time brain magnetic resonance spectroscopic imaging (MRSI) data by applying a model-based canonical correlation analyses algorithm and by using, as prior knowledge, multimodal sources of information coming from high-resolution magic angle spinning (HR-MAS), MRSI and magnetic resonance imaging. The potential and limitations of fusing in vivo and ex vivo nuclear magnetic resonance sources to detect brain tumors is investigated. We present various modalities for multimodal data fusion, study the effect and the impact of using multimodal information for classifying MRSI brain glial tumors data and analyze which parameters influence the classification results by means of extensive simulation and in vivo studies. Special attention is drawn to the possibility of considering HR-MAS data as a complementary dataset when dealing with a lack of MRSI data needed to build a classifier. Results show that HR-MAS information can have added value in the process of classifying MRSI data

Evaluation of classifiers that score linear type traits and body condition score using common sires

NARCIS (Netherlands)

Veerkamp, R.F.; Gerritsen, C.L.M.; Koenen, E.P.C.; Hamoen, A.; Jong, de G.

2002-01-01

Subjective visual assessment of animals by classifiers is undertaken for several different traits in farm livestock, e.g., linear type traits, body condition score, or carcass conformation. One of the difficulties in assessment is the effect of an individual classifier. To ensure that classifiers

Detection and Classification of Transformer Winding Mechanical Faults Using UWB Sensors and Bayesian Classifier

Science.gov (United States)

Alehosseini, Ali; A. Hejazi, Maryam; Mokhtari, Ghassem; B. Gharehpetian, Gevork; Mohammadi, Mohammad

2015-06-01

In this paper, the Bayesian classifier is used to detect and classify the radial deformation and axial displacement of transformer windings. The proposed method is tested on a model of transformer for different volumes of radial deformation and axial displacement. In this method, ultra-wideband (UWB) signal is sent to the simplified model of the transformer winding. The received signal from the winding model is recorded and used for training and testing of Bayesian classifier in different axial displacement and radial deformation states of the winding. It is shown that the proposed method has a good accuracy to detect and classify the axial displacement and radial deformation of the winding.

Aspectual Morphemes as Verb Classifiers in Slavic and Non-Slavic Languages

OpenAIRE

Menzenski, Matthew

2014-01-01

This paper was presented at the Slavic Linguistics Society Annual Meeting in Seattle, Washington on September 19 2014.   Abstract: Janda et al. (2013) propose an analysis of Russian aspectual prefixes as verb classifiers, arguing that the prefix which forms the 'natural perfective' from a given verb serves to classify that verb according to its semantic characteristics. This analysis contrasts with the traditional analysis of Russian aspect, described by Tixonov (1998) and others, in...

Learning classifier systems with memory condition to solve non-Markov problems

OpenAIRE

Zang, Zhaoxiang; Li, Dehua; Wang, Junying

2012-01-01

In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fail...

Diagnosis of Broiler Livers by Classifying Image Patches

DEFF Research Database (Denmark)

Jørgensen, Anders; Fagertun, Jens; Moeslund, Thomas B.

2017-01-01

The manual health inspection are becoming the bottleneck at poultry processing plants. We present a computer vision method for automatic diagnosis of broiler livers. The non-rigid livers, of varying shape and sizes, are classified in patches by a convolutional neural network, outputting maps...

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

Science.gov (United States)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Strategic planning for health care management information systems.

Science.gov (United States)

Rosenberger, H R; Kaiser, K M

1985-01-01

Using a planning methodology and a structured design technique for analyzing data and data flow, information requirements can be derived to produce a strategic plan for a management information system. Such a long-range plan classifies information groups and assigns them priorities according to the goals of the organization. The approach emphasizes user involvement.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Science.gov (United States)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

On minimal inhibitory rules for almost all k-valued information systems

KAUST Repository

Moshkov, Mikhail; Skowron, Andrzej; Suraj, Zbigniew

2009-01-01

The minimal inhibitory rules for information systems can be used for construction of classifiers. We show that almost all information systems from a certain large class of information systems have relatively short minimal inhibitory rules. However

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

Science.gov (United States)

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

10 CFR 73.22 - Protection of Safeguards Information: Specific requirements.

Science.gov (United States)

2010-01-01

... protection. Information not classified as Restricted Data or National Security Information related to physical protection, including: (i) The composite physical security plan for the facility or site; (ii... Safeguards Information. (2) Each computer not located within an approved and lockable security storage...

Cascaded lexicalised classifiers for second-person reference resolution

NARCIS (Netherlands)

Purver, M.; Fernández, R.; Frampton, M.; Peters, S.; Healey, P.; Pieraccini, R.; Byron, D.; Young, S.; Purver, M.

2009-01-01

This paper examines the resolution of the second person English pronoun you in multi-party dialogue. Following previous work, we attempt to classify instances as generic or referential, and in the latter case identify the singular or plural addressee. We show that accuracy and robustness can be

Final Validation of the ProMisE Molecular Classifier for Endometrial Carcinoma in a Large Population-based Case Series.

Science.gov (United States)

Kommoss, S; McConechy, M K; Kommoss, F; Leung, S; Bunz, A; Magrill, J; Britton, H; Kommoss, F; Grevenkamp, F; Karnezis, A; Yang, W; Lum, A; Krämer, B; Taran, F; Staebler, A; Lax, S; Brucker, S Y; Huntsman, D G; Gilks, C B; McAlpine, J N; Talhouk, A

2018-02-07

Based on The Cancer Genome Atlas, we previously developed and confirmed a pragmatic molecular classifier for endometrial cancers; ProMisE (Proactive Molecular Risk Classifier for Endometrial Cancer). ProMisE identifies four prognostically distinct molecular subtypes, and can be applied to diagnostic specimens (biopsy/curettings), enabling earlier informed decision-making. We have strictly adhered to the Institute of Medicine (IOM) guidelines for the development of genomic biomarkers, and herein present the final validation step of a locked-down classifier prior to clinical application. We assessed a retrospective cohort of women from the Tübingen University Women's Hospital treated for endometrial carcinoma between 2003-13. Primary outcomes of overall, disease-specific and progression-free survival were evaluated for clinical, pathological, and molecular features. Complete clinical and molecular data were evaluable from 452 women. Patient age ranged from 29 - 93 (median 65) years, and 87.8% cases were endometrioid histotype. Grade distribution included 282 (62.4%) G1, 75 (16.6%) G2, and 95 (21.0%) G3 tumors. 276 (61.1%) patients had stage IA disease, with the remaining stage IB (89 (19.7%)), stage II (26 (5.8%)), and stage III/IV (61 (13.5%)). ProMisE molecular classification yielded 127 (28.1%) MMR-D, 42 (9.3%) POLE, 55 (12.2%) p53abn, and 228 (50.4%) p53wt. ProMisE was a prognostic marker for progression-free (P=0.001) and disease-specific (P=0.03) survival even after adjusting for known risk factors. Concordance between diagnostic and surgical specimens was highly favorable; accuracy 0.91, kappa 0.88. We have developed, confirmed and now validated a pragmatic molecular classification tool (ProMisE) that provides consistent categorization of tumors and identifies four distinct prognostic molecular subtypes. ProMisE can be applied to diagnostic samples and thus could be used to inform surgical procedure(s) and/or need for adjuvant therapy. Based on the IOM

An improved early detection method of type-2 diabetes mellitus using multiple classifier system

KAUST Repository

Zhu, Jia

2015-01-01

The specific causes of complex diseases such as Type-2 Diabetes Mellitus (T2DM) have not yet been identified. Nevertheless, many medical science researchers believe that complex diseases are caused by a combination of genetic, environmental, and lifestyle factors. Detection of such diseases becomes an issue because it is not free from false presumptions and is accompanied by unpredictable effects. Given the greatly increased amount of data gathered in medical databases, data mining has been used widely in recent years to detect and improve the diagnosis of complex diseases. However, past research showed that no single classifier can be considered optimal for all problems. Therefore, in this paper, we focus on employing multiple classifier systems to improve the accuracy of detection for complex diseases, such as T2DM. We proposed a dynamic weighted voting scheme called multiple factors weighted combination for classifiers\\' decision combination. This method considers not only the local and global accuracy but also the diversity among classifiers and localized generalization error of each classifier. We evaluated our method on two real T2DM data sets and other medical data sets. The favorable results indicated that our proposed method significantly outperforms individual classifiers and other fusion methods.

48 CFR 3004.470 - Security requirements for access to unclassified facilities, Information Technology resources...

Science.gov (United States)

2010-10-01

... access to unclassified facilities, Information Technology resources, and sensitive information. 3004.470... Technology resources, and sensitive information. ... ACQUISITION REGULATION (HSAR) GENERAL ADMINISTRATIVE MATTERS Safeguarding Classified and Sensitive Information...

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

Directory of Open Access Journals (Sweden)

Keith Jonathan M

2012-07-01

Full Text Available Abstract Background Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. Results We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. Conclusions The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be

Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

Science.gov (United States)

Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar

2015-02-01

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.

100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox.

Directory of Open Access Journals (Sweden)

Francisco J Valverde-Albacete

Full Text Available The most widely spread measure of performance, accuracy, suffers from a paradox: predictive models with a given level of accuracy may have greater predictive power than models with higher accuracy. Despite optimizing classification error rate, high accuracy models may fail to capture crucial information transfer in the classification task. We present evidence of this behavior by means of a combinatorial analysis where every possible contingency matrix of 2, 3 and 4 classes classifiers are depicted on the entropy triangle, a more reliable information-theoretic tool for classification assessment. Motivated by this, we develop from first principles a measure of classification performance that takes into consideration the information learned by classifiers. We are then able to obtain the entropy-modulated accuracy (EMA, a pessimistic estimate of the expected accuracy with the influence of the input distribution factored out, and the normalized information transfer factor (NIT, a measure of how efficient is the transmission of information from the input to the output set of classes. The EMA is a more natural measure of classification performance than accuracy when the heuristic to maximize is the transfer of information through the classifier instead of classification error count. The NIT factor measures the effectiveness of the learning process in classifiers and also makes it harder for them to "cheat" using techniques like specialization, while also promoting the interpretability of results. Their use is demonstrated in a mind reading task competition that aims at decoding the identity of a video stimulus based on magnetoencephalography recordings. We show how the EMA and the NIT factor reject rankings based in accuracy, choosing more meaningful and interpretable classifiers.

Stability of halophilic proteins: from dipeptide attributes to discrimination classifier.

Science.gov (United States)

Zhang, Guangya; Huihua, Ge; Yi, Lin

2013-02-01

To investigate the molecular features responsible for protein halophilicity is of great significance for understanding the structure basis of protein halo-stability and would help to develop a practical strategy for designing halophilic proteins. In this work, we have systematically analyzed the dipeptide composition of the halophilic and non-halophilic protein sequences. We observed the halophilic proteins contained more DA, RA, AD, RR, AP, DD, PD, EA, VG and DV at the expense of LK, IL, II, IA, KK, IS, KA, GK, RK and AI. We identified some macromolecular signatures of halo-adaptation, and thought the dipeptide composition might contain more information than amino acid composition. Based on the dipeptide composition, we have developed a machine learning method for classifying halophilic and non-halophilic proteins for the first time. The accuracy of our method for the training dataset was 100.0%, and for the 10-fold cross-validation was 93.1%. We also discussed the influence of some specific dipeptides on prediction accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Using decision-tree classifier systems to extract knowledge from databases

Science.gov (United States)

St.clair, D. C.; Sabharwal, C. L.; Hacke, Keith; Bond, W. E.

1990-01-01

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described.

Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions

Directory of Open Access Journals (Sweden)

Altsybeev Igor

2017-01-01

Full Text Available Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single detector (either on the signal in zero-degree calorimeters or on the multiplicity in some semi-central rapidity range. In the present work, we made an attempt to develop an approach for centrality determination that is based on machine-learning techniques and utilizes information from several detector subsystems simultaneously. Different event classifiers are suggested and evaluated for their selectivity power in terms of the number of nucleons-participants and the impact parameter of the collision. Finer centrality resolution may allow to reduce impact from so-called volume fluctuations on physical observables being studied in heavy-ion experiments like ALICE at the LHC and fixed target experiment NA61/SHINE on SPS.

SparCLeS: dynamic l₁ sparse classifiers with level sets for robust beard/moustache detection and segmentation.

Science.gov (United States)

Le, T Hoang Ngan; Luu, Khoa; Savvides, Marios

2013-08-01

Robust facial hair detection and segmentation is a highly valued soft biometric attribute for carrying out forensic facial analysis. In this paper, we propose a novel and fully automatic system, called SparCLeS, for beard/moustache detection and segmentation in challenging facial images. SparCLeS uses the multiscale self-quotient (MSQ) algorithm to preprocess facial images and deal with illumination variation. Histogram of oriented gradients (HOG) features are extracted from the preprocessed images and a dynamic sparse classifier is built using these features to classify a facial region as either containing skin or facial hair. A level set based approach, which makes use of the advantages of both global and local information, is then used to segment the regions of a face containing facial hair. Experimental results demonstrate the effectiveness of our proposed system in detecting and segmenting facial hair regions in images drawn from three databases, i.e., the NIST Multiple Biometric Grand Challenge (MBGC) still face database, the NIST Color Facial Recognition Technology FERET database, and the Labeled Faces in the Wild (LFW) database.

The use and effectiveness of information system development methodologies in health information systems / Pieter Wynand Conradie.

OpenAIRE

Conradie, Pieter Wynand

2010-01-01

Abstract The main focus of this study is the identification of factors influencing the use and effectiveness of information system development methodologies (Le., systems development methodologies) in health information systems. In essence, it can be viewed as exploratory research, utilizing a conceptual research model to investigate the relationships among the hypothesised factors. More specifically, classified as behavioural science, it combines two theoretical models, namely...

Localization and Recognition of Dynamic Hand Gestures Based on Hierarchy of Manifold Classifiers

Science.gov (United States)

Favorskaya, M.; Nosov, A.; Popov, A.

2015-05-01

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset "Multi-modal Gesture Recognition Challenge 2013: Dataset and Results" including 393 dynamic hand-gestures was chosen. The proposed method yielded 84-91% recognition accuracy, in average, for restricted set of dynamic gestures.

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

Directory of Open Access Journals (Sweden)

M. Favorskaya

2015-05-01

Full Text Available Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case. Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset “Multi-modal Gesture Recognition Challenge 2013: Dataset and Results” including 393 dynamic hand-gestures was chosen. The proposed method yielded 84–91% recognition accuracy, in average, for restricted set of dynamic gestures.

Security Enrichment in Intrusion Detection System Using Classifier Ensemble

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Uma R. Salunkhe

2017-01-01

Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

Interface Prostheses With Classifier-Feedback-Based User Training.

Science.gov (United States)

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well

Application of the Naive Bayesian Classifier to optimize treatment decisions

International Nuclear Information System (INIS)

Kazmierska, Joanna; Malicki, Julian

2008-01-01

Background and purpose: To study the accuracy, specificity and sensitivity of the Naive Bayesian Classifier (NBC) in the assessment of individual risk of cancer relapse or progression after radiotherapy (RT). Materials and methods: Data of 142 brain tumour patients irradiated from 2000 to 2005 were analyzed. Ninety-six attributes related to disease, patient and treatment were chosen. Attributes in binary form consisted of the training set for NBC learning. NBC calculated an individual conditional probability of being assigned to: relapse or progression (1), or no relapse or progression (0) group. Accuracy, attribute selection and quality of classifier were determined by comparison with actual treatment results, leave-one-out and cross validation methods, respectively. Clinical setting test utilized data of 35 patients. Treatment results at classification were unknown and were compared with classification results after 3 months. Results: High classification accuracy (84%), specificity (0.87) and sensitivity (0.80) were achieved, both for classifier training and in progressive clinical evaluation. Conclusions: NBC is a useful tool to support the assessment of individual risk of relapse or progression in patients diagnosed with brain tumour undergoing RT postoperatively

Abbreviations: Their Effects on Comprehension of Classified Advertisements.

Science.gov (United States)

Sokol, Kirstin R.

Two experimental designs were used to test the hypothesis that abbreviations in classified advertisements decrease the reader's comprehension of such ads. In the first experimental design, 73 high school students read four ads (for employment, used cars, apartments for rent, and articles for sale) either with abbreviations or with all…

Glycosyltransferase Gene Expression Profiles Classify Cancer Types and Propose Prognostic Subtypes

Science.gov (United States)

Ashkani, Jahanshah; Naidoo, Kevin J.

2016-05-01

Aberrant glycosylation in tumours stem from altered glycosyltransferase (GT) gene expression but can the expression profiles of these signature genes be used to classify cancer types and lead to cancer subtype discovery? The differential structural changes to cellular glycan structures are predominantly regulated by the expression patterns of GT genes and are a hallmark of neoplastic cell metamorphoses. We found that the expression of 210 GT genes taken from 1893 cancer patient samples in The Cancer Genome Atlas (TCGA) microarray data are able to classify six cancers; breast, ovarian, glioblastoma, kidney, colon and lung. The GT gene expression profiles are used to develop cancer classifiers and propose subtypes. The subclassification of breast cancer solid tumour samples illustrates the discovery of subgroups from GT genes that match well against basal-like and HER2-enriched subtypes and correlates to clinical, mutation and survival data. This cancer type glycosyltransferase gene signature finding provides foundational evidence for the centrality of glycosylation in cancer.

Classifying Aging as a Disease in the context of ICD-11

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Alex eZhavoronkov

2015-11-01

Full Text Available Aging is a complex continuous multifactorial process leading to loss of function and crystalizing into the many age-related diseases. Here, we explore the arguments for classifying aging as a disease in the context of the upcoming World Health Organization’s 11th International Statistical Classification of Diseases and Related Health Problems (ICD-11, expected to be finalized in 2018. We hypothesize that classifying aging as a disease will result in new approaches and business models for addressing aging as a treatable condition, which will lead to both economic and healthcare benefits for all stakeholders. Classification of aging as a disease may lead to more efficient allocation of resources by enabling funding bodies and other stakeholders to use quality-adjusted life years (QALYs and healthy-years equivalent (HYE as metrics when evaluating both research and clinical programs. We propose forming a Task Force to interface the WHO in order to develop a multidisciplinary framework for classifying aging as a disease.

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

Science.gov (United States)

Islam, Md Rabiul

2014-01-01

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

Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

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Md. Rabiul Islam

2014-01-01

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

Use of classifier to determine coffee harvest time by detachment force

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Murilo M. de Barros

Full Text Available ABSTRACT Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Science.gov (United States)

da Silva, Flávio Altinier Maximiano; Pedrini, Helio

2015-03-01

Facial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

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.

An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

Science.gov (United States)

Yu, Hualong; Ni, Jun

2014-01-01

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

Science.gov (United States)

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

2014-01-01

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

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.

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Sun Hee Ahn

Full Text Available Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host's inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection and was validated in outbred mice (AUC>0.97. A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI from healthy subjects (AUC 0.99 and E. coli BSI (AUC 0.84. Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84. Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively. The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Multimodal fusion of polynomial classifiers for automatic person recgonition

Science.gov (United States)

Broun, Charles C.; Zhang, Xiaozheng

2001-03-01

With the prevalence of the information age, privacy and personalization are forefront in today's society. As such, biometrics are viewed as essential components of current evolving technological systems. Consumers demand unobtrusive and non-invasive approaches. In our previous work, we have demonstrated a speaker verification system that meets these criteria. However, there are additional constraints for fielded systems. The required recognition transactions are often performed in adverse environments and across diverse populations, necessitating robust solutions. There are two significant problem areas in current generation speaker verification systems. The first is the difficulty in acquiring clean audio signals in all environments without encumbering the user with a head- mounted close-talking microphone. Second, unimodal biometric systems do not work with a significant percentage of the population. To combat these issues, multimodal techniques are being investigated to improve system robustness to environmental conditions, as well as improve overall accuracy across the population. We propose a multi modal approach that builds on our current state-of-the-art speaker verification technology. In order to maintain the transparent nature of the speech interface, we focus on optical sensing technology to provide the additional modality-giving us an audio-visual person recognition system. For the audio domain, we use our existing speaker verification system. For the visual domain, we focus on lip motion. This is chosen, rather than static face or iris recognition, because it provides dynamic information about the individual. In addition, the lip dynamics can aid speech recognition to provide liveness testing. The visual processing method makes use of both color and edge information, combined within Markov random field MRF framework, to localize the lips. Geometric features are extracted and input to a polynomial classifier for the person recognition process. A late

Advanced recognition of explosives in traces on polymer surfaces using LIBS and supervised learning classifiers.

Science.gov (United States)

Serrano, Jorge; Moros, Javier; Sánchez, Carlos; Macías, Jorge; Laserna, J Javier

2014-01-02

The large similarity existing in the spectral emissions collected from organic compounds by laser-induced breakdown spectroscopy (LIBS) is a limiting factor for the use of this technology in the real world. Specifically, among the most ambitious challenges of today's LIBS involves the recognition of an organic residue when neglected on the surface of an object of identical nature. Under these circumstances, the development of an efficient algorithm to disclose the minute differences within this highly complex spectral information is crucial for a realistic application of LIBS in countering explosive threats. An approach cemented on scatter plots of characteristic emission features has been developed to identify organic explosives when located on polymeric surfaces (teflon, nylon and polyethylene). By using selected spectral variables, the approach allows to design a concise classifier for alerting when one of four explosives (DNT, TNT, RDX and PETN) is present on the surface of the polymer. Ordinary products (butter, fuel oil, hand cream, olive oil and motor oil) cause no confusion in the decisions taken by the classifier. With rates of false negatives and false positives below 5%, results demonstrate that the classification algorithm enables to label residues according to their harmful nature in the most demanding scenario for a LIBS sensor. Copyright © 2013 Elsevier B.V. All rights reserved.

A Two-Step Resume Information Extraction Algorithm

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Jie Chen

2018-01-01

Full Text Available With the rapid growth of Internet-based recruiting, there are a great number of personal resumes among recruiting systems. To gain more attention from the recruiters, most resumes are written in diverse formats, including varying font size, font colour, and table cells. However, the diversity of format is harmful to data mining, such as resume information extraction, automatic job matching, and candidates ranking. Supervised methods and rule-based methods have been proposed to extract facts from resumes, but they strongly rely on hierarchical structure information and large amounts of labelled data, which are hard to collect in reality. In this paper, we propose a two-step resume information extraction approach. In the first step, raw text of resume is identified as different resume blocks. To achieve the goal, we design a novel feature, Writing Style, to model sentence syntax information. Besides word index and punctuation index, word lexical attribute and prediction results of classifiers are included in Writing Style. In the second step, multiple classifiers are employed to identify different attributes of fact information in resumes. Experimental results on a real-world dataset show that the algorithm is feasible and effective.

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.

Legal Regimes of Official Information in Ukraine

Directory of Open Access Journals (Sweden)

Serhii Yesimov

2018-04-01

Full Text Available In the article on the basis of the methodology of system analysis the legal nature and sources of legal regulation of the legal regime of official information in Ukraine in the conditions of adaptation of Ukrainian legislation to the legislation of the European Union are considered. A comparative legal analysis of official information in the public-law and private-law spheres in the context of legal regimes of restricted information, confidential information and information classified as state secrets has been conducted.

High speed intelligent classifier of tomatoes by colour, size and weight

Energy Technology Data Exchange (ETDEWEB)

Cement, J.; Novas, N.; Gazquez, J. A.; Manzano-Agugliaro, F.

2012-11-01

At present most horticultural products are classified and marketed according to quality standards, which provide a common language for growers, packers, buyers and consumers. The standardisation of both product and packaging enables greater speed and efficiency in management and marketing. Of all the vegetables grown in greenhouses, tomatoes are predominant in both surface area and tons produced. This paper will present the development and evaluation of a low investment classification system of tomatoes with these objectives: to put it at the service of producing farms and to classify for trading standards. An intelligent classifier of tomatoes has been developed by weight, diameter and colour. This system has optimised the necessary algorithms for data processing in the case of tomatoes, so that productivity is greatly increased, with the use of less expensive and lower performance electronics. The prototype is able to achieve very high speed classification, 12.5 ratings per second, using accessible and low cost commercial equipment for this. It decreases fourfold the manual sorting time and is not sensitive to the variety of tomato classified. This system facilitates the processes of standardisation and quality control, increases the competitiveness of tomato farms and impacts positively on profitability. The automatic classification system described in this work represents a contribution from the economic point of view, as it is profitable for a farm in the short term (less than six months), while the existing systems, can only be used in large trading centers. (Author) 36 refs.

46 CFR 108.187 - Ventilation for brush type electric motors in classified spaces.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Ventilation for brush type electric motors in classified... Ventilation for brush type electric motors in classified spaces. Ventilation for brush type electric motors in... Electrical Equipment in Hazardous Locations”, except audible and visual alarms may be used if shutting down...

Classified model and characteristics of strategies at tourist companies

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I.V. Saukh

2017-12-01

Full Text Available The research is devoted to the assessment of the scientific approaches to the identification of classification features of the strategy and its types distinguished in accordance with the mentioned features. The research object is the activities of tourist companies and this determines the choice of strategies typical for the tourism field. It is substantiated that the scientific approaches to the classification of strategies are various in specific literature because of obscurity in the strategy definition, vagueness and plurality of its classified features. Due to the current research the authors have improved the classified model of strategies for tourist companies that will result in making effective management decisions directed to the development of enterprise potential under conditions of unstable and unpredictable external environment. The paper singles out the peculiarities of functioning the tourism branch, which are the following : high sensitivity to the changes in external environment; the high level of competition in the field; dynamics and the lack of necessity for the use of «far-seeing» strategies; insufficiency of information provision for the application of traditional western models and matric methods of strategy development; time gap between obtaining the service and its consumption; a great number of intermediaries; seasonal swings in demands; the sudden shift of external environment caused by cyclicity, globalization, political decisions of separate countries and etc. The article shows essential differences in the development of financial strategies of small-scale enterprises and stock companies of tourist business. It is substantiated that small-scale enterprises develop strategies directed to a higher level of personal services, occupational competence, ability and experience in designing, the best knowledge of regional conditions and flexible decisions caused by the peculiarities of the received orders. Taking into

18 CFR 367.18 - Criteria for classifying leases.

Science.gov (United States)

2010-04-01

... the lessee) must not give rise to a new classification of a lease for accounting purposes. ... classifying the lease. (4) The present value at the beginning of the lease term of the minimum lease payments... taxes to be paid by the lessor, including any related profit, equals or exceeds 90 percent of the excess...

Selectivity in Genetic Association with Sub-classified Migraine in Women

Science.gov (United States)

Chasman, Daniel I.; Anttila, Verneri; Buring, Julie E.; Ridker, Paul M.; Schürks, Markus; Kurth, Tobias

2014-01-01

Migraine can be sub-classified not only according to presence of migraine aura (MA) or absence of migraine aura (MO), but also by additional features accompanying migraine attacks, e.g. photophobia, phonophobia, nausea, etc. all of which are formally recognized by the International Classification of Headache Disorders. It remains unclear how aura status and the other migraine features may be related to underlying migraine pathophysiology. Recent genome-wide association studies (GWAS) have identified 12 independent loci at which single nucleotide polymorphisms (SNPs) are associated with migraine. Using a likelihood framework, we explored the selective association of these SNPs with migraine, sub-classified according to aura status and the other features in a large population-based cohort of women including 3,003 active migraineurs and 18,108 free of migraine. Five loci met stringent significance for association with migraine, among which four were selective for sub-classified migraine, including rs11172113 (LRP1) for MO. The number of loci associated with migraine increased to 11 at suggestive significance thresholds, including five additional selective associations for MO but none for MA. No two SNPs showed similar patterns of selective association with migraine characteristics. At one extreme, SNPs rs6790925 (near TGFBR2) and rs2274316 (MEF2D) were not associated with migraine overall, MA, or MO but were selective for migraine sub-classified by the presence of one or more of the additional migraine features. In contrast, SNP rs7577262 (TRPM8) was associated with migraine overall and showed little or no selectivity for any of the migraine characteristics. The results emphasize the multivalent nature of migraine pathophysiology and suggest that a complete understanding of the genetic influence on migraine may benefit from analyses that stratify migraine according to both aura status and the additional diagnostic features used for clinical characterization of

Speaker gender identification based on majority vote classifiers

Science.gov (United States)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

Informization Implementation for Chinese Retailers

Institute of Scientific and Technical Information of China (English)

ZHU Yan; LI Yan; QIAN Yu; CHEN Jianfeng; CHEN Jian

2008-01-01

Retailing is an important component of every country's economic system. The current status and developments in the informization of Chinese retail industry were investigated by using questionnaires and interviews to survey 139 retailers throughout China. The investigation shows that Chinese retailers are in the initial informization stage, and can be classified into different types with corresponding informization characteristics. In addition, the survey identified the key problems faced by retailers in the initial stage. Developments in the information technology field were analyzed to identify the key technologies that Chinese retailers should focus on during the informization process. The investigation also shows that the retailers have not arrived at a consensus about information technology adoption, and thus hesitate to use new information technologies, such as the radio frequency identification.

Cubical sets as a classifying topos

DEFF Research Database (Denmark)

Spitters, Bas

Coquand’s cubical set model for homotopy type theory provides the basis for a computational interpretation of the univalence axiom and some higher inductive types, as implemented in the cubical proof assistant. We show that the underlying cube category is the opposite of the Lawvere theory of De...... Morgan algebras. The topos of cubical sets itself classifies the theory of ‘free De Morgan algebras’. This provides us with a topos with an internal ‘interval’. Using this interval we construct a model of type theory following van den Berg and Garner. We are currently investigating the precise relation...

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

Science.gov (United States)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples

Identification and optimization of classifier genes from multi-class earthworm microarray dataset.

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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.

Classifying patients' complaints for regulatory purposes : A Pilot Study

NARCIS (Netherlands)

Bouwman, R.J.R.; Bomhoff, Manja; Robben, Paul; Friele, R.D.

2018-01-01

Objectives: It is assumed that classifying and aggregated reporting of patients' complaints by regulators helps to identify problem areas, to respond better to patients and increase public accountability. This pilot study addresses what a classification of complaints in a regulatory setting

Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

Directory of Open Access Journals (Sweden)

Zhibin Xiao

2017-02-01

Full Text Available Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.

Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

Science.gov (United States)

Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad

2018-04-01

There are many ways of implementing the use of sentiments often found in documents; one of which is the sentiments found on the product or service reviews. It is so important to be able to process and extract textual data from the documents. Therefore, we propose a system that is able to classify sentiments from review documents into two classes: positive sentiment and negative sentiment. We use Naive Bayes Classifier method in this document classification system that we build. We choose Movienthusiast, a movie reviews in Bahasa Indonesia website as the source of our review documents. From there, we were able to collect 1201 movie reviews: 783 positive reviews and 418 negative reviews that we use as the dataset for this machine learning classifier. The classifying accuracy yields an average of 88.37% from five times of accuracy measuring attempts using aforementioned dataset.

Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls

Directory of Open Access Journals (Sweden)

Deanna eGreenstein

2012-06-01

Full Text Available Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI. However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest, we classified 98 COS patients and 99 age, sex, and ethnicity-matched healthy controls. We also used Random Forest to determine the likelihood of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p= 0.0004 and fewer developmental delays (p=0.02. Presence of copy number variation (CNV was associated with lower probability of being classified as schizophrenia (p=0.001. The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusions: Schizophrenia and control groups can be well classified using Random Forest and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.

Grounding grammatical categories: attention bias in hand space influences grammatical congruency judgment of Chinese nominal classifiers.

Science.gov (United States)

Lobben, Marit; D'Ascenzo, Stefania

2015-01-01

Embodied cognitive theories predict that linguistic conceptual representations are grounded and continually represented in real world, sensorimotor experiences. However, there is an on-going debate on whether this also holds for abstract concepts. Grammar is the archetype of abstract knowledge, and therefore constitutes a test case against embodied theories of language representation. Former studies have largely focussed on lexical-level embodied representations. In the present study we take the grounding-by-modality idea a step further by using reaction time (RT) data from the linguistic processing of nominal classifiers in Chinese. We take advantage of an independent body of research, which shows that attention in hand space is biased. Specifically, objects near the hand consistently yield shorter RTs as a function of readiness for action on graspable objects within reaching space, and the same biased attention inhibits attentional disengagement. We predicted that this attention bias would equally apply to the graspable object classifier but not to the big object classifier. Chinese speakers (N = 22) judged grammatical congruency of classifier-noun combinations in two conditions: graspable object classifier and big object classifier. We found that RTs for the graspable object classifier were significantly faster in congruent combinations, and significantly slower in incongruent combinations, than the big object classifier. There was no main effect on grammatical violations, but rather an interaction effect of classifier type. Thus, we demonstrate here grammatical category-specific effects pertaining to the semantic content and by extension the visual and tactile modality of acquisition underlying the acquisition of these categories. We conclude that abstract grammatical categories are subjected to the same mechanisms as general cognitive and neurophysiological processes and may therefore be grounded.

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Science.gov (United States)

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

Science.gov (United States)

Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

2014-01-01

The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

Fuzzy Search Method for Hi Education Information Security

Directory of Open Access Journals (Sweden)

Grigory Grigorevich Novikov

2016-03-01

Full Text Available The main reason of the research is how to use fuzzy search method for information security of Hi Education or some similar purposes. So many sensitive information leaks are through non SUMMARY 149 classified documents legal publishing. That’s why many intelligence services so love to use the «mosaic» information collection method. This article is about how to prevent it.

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.

Comparisons and Selections of Features and Classifiers for Short Text Classification

Science.gov (United States)

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

2017-10-01

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

Classifying aging as a disease in the context of ICD-11.

Science.gov (United States)

Zhavoronkov, Alex; Bhullar, Bhupinder

2015-01-01

Aging is a complex continuous multifactorial process leading to loss of function and crystalizing into the many age-related diseases. Here, we explore the arguments for classifying aging as a disease in the context of the upcoming World Health Organization's 11th International Statistical Classification of Diseases and Related Health Problems (ICD-11), expected to be finalized in 2018. We hypothesize that classifying aging as a disease with a "non-garbage" set of codes will result in new approaches and business models for addressing aging as a treatable condition, which will lead to both economic and healthcare benefits for all stakeholders. Actionable classification of aging as a disease may lead to more efficient allocation of resources by enabling funding bodies and other stakeholders to use quality-adjusted life years (QALYs) and healthy-years equivalent (HYE) as metrics when evaluating both research and clinical programs. We propose forming a Task Force to interface the WHO in order to develop a multidisciplinary framework for classifying aging as a disease with multiple disease codes facilitating for therapeutic interventions and preventative strategies.

Should OCD be classified as an anxiety disorder in DSM-V?

Science.gov (United States)

Stein, Dan J; Fineberg, Naomi A; Bienvenu, O Joseph; Denys, Damiaan; Lochner, Christine; Nestadt, Gerald; Leckman, James F; Rauch, Scott L; Phillips, Katharine A

2010-06-01

In DSM-III, DSM-III-R, and DSM-IV, obsessive-compulsive disorder (OCD) was classified as an anxiety disorder. In ICD-10, OCD is classified separately from the anxiety disorders, although within the same larger category as anxiety disorders (as one of the "neurotic, stress-related, and somatoform disorders"). Ongoing advances in our understanding of OCD and other anxiety disorders have raised the question of whether OCD should continue to be classified with the anxiety disorders in DSM-V. This review presents a number of options and preliminary recommendations to be considered for DSM-V. Evidence is reviewed for retaining OCD in the category of anxiety disorders, and for moving OCD to a separate category of obsessive-compulsive (OC)-spectrum disorders, if such a category is included in DSM-V. Our preliminary recommendation is that OCD be retained in the category of anxiety disorders but that this category also includes OC-spectrum disorders along with OCD. If this change is made, the name of this category should be changed to reflect this proposed change. (c) 2010 Wiley-Liss, Inc.

Defining and Classifying Interest Groups

DEFF Research Database (Denmark)

Baroni, Laura; Carroll, Brendan; Chalmers, Adam

2014-01-01

The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...... in the organizational attributes of specific interest group types. As expected, our comparison of coding schemes reveals a closer link between group attributes and group type in narrower classification schemes based on group organizational characteristics than those based on a behavioral definition of lobbying....

New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities.

Science.gov (United States)

Rebouças Filho, Pedro P; Sarmento, Róger Moura; Holanda, Gabriel Bandeira; de Alencar Lima, Daniel

2017-09-01

Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity. Stroke is an emergency problem for which early identification and measures are difficult; however, computer-aided diagnoses (CAD) can play an important role in obtaining information imperceptible to the human eye. Thus, this work proposes a new method for extracting features based on radiological density patterns of the brain, called Analysis of Brain Tissue Density (ABTD). The proposed method is a specific approach applied to CT images to identify and classify the occurrence of stroke diseases. The evaluation of the results of the ABTD extractor proposed in this paper were compared with extractors already established in the literature, such as features from Gray-Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), Central Moments (CM), Statistical Moments (SM), Hu's Moment (HM) and Zernike's Moments (ZM). Using a database of 420 CT images of the skull, each extractor was applied with the classifiers such as MLP, SVM, kNN, OPF and Bayesian to classify if a CT image represented a healthy brain or one with an ischemic or hemorrhagic stroke. ABTD had the shortest extraction time and the highest average accuracy (99.30%) when combined with OPF using the Euclidean distance. Also, the average accuracy values for all classifiers were higher than 95%. The relevance of the results demonstrated that the ABTD method is a useful algorithm to extract features that can potentially be integrated with CAD systems to assist in stroke diagnosis. Copyright © 2017 Elsevier B.V. All rights

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

Science.gov (United States)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post

48 CFR 1804.470 - Security requirements for unclassified information technology (IT) resources.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Security requirements for unclassified information technology (IT) resources. 1804.470 Section 1804.470 Federal Acquisition Regulations... Classified Information Within Industry 1804.470 Security requirements for unclassified information technology...

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

OpenAIRE

Rosenberg, Ishai; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval

2017-01-01

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such...

Discussion of a method for providing general risk information by linking with the nuclear information

International Nuclear Information System (INIS)

Shobu, Nobuhiro; Yokomizo, Shirou; Umezawa, Sayaka

2004-06-01

'Risk information navigator (http://www.ricotti.jp/risknavi/)', an internet tool for arousing public interest and fostering people's risk literacy, has been developed as the contents for the official website of Techno Community Square 'RICOTTI' (http://www.ricotti.jp) at TOKAI village. In this report we classified the risk information into the fields, Health/Daily Life', 'Society/Crime/Disaster' and Technology/Environment/Energy', for the internet tool contents. According to these categories we discussed a method for providing various risk information on general fields by linking with the information on nuclear field. The web contents are attached to this report with the CD-R media. (author)

Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory.

Science.gov (United States)

Qiu, Wang-Ren; Zheng, Quan-Shu; Sun, Bi-Qian; Xiao, Xuan

2017-03-01

Predicting phosphorylation protein is a challenging problem, particularly when query proteins have multi-label features meaning that they may be phosphorylated at two or more different type amino acids. In fact, human protein usually be phosphorylated at serine, threonine and tyrosine. By introducing the "multi-label learning" approach, a novel predictor has been developed that can be used to deal with the systems containing both single- and multi-label phosphorylation protein. Here we proposed a predictor called Multi-iPPseEvo by (1) incorporating the protein sequence evolutionary information into the general pseudo amino acid composition (PseAAC) via the grey system theory, (2) balancing out the skewed training datasets by the asymmetric bootstrap approach, and (3) constructing an ensemble predictor by fusing an array of individual random forest classifiers thru a voting system. Rigorous cross-validations via a set of multi-label metrics indicate that the multi-label phosphorylation predictor is very promising and encouraging. The current approach represents a new strategy to deal with the multi-label biological problems, and the software is freely available for academic use at http://www.jci-bioinfo.cn/Multi-iPPseEvo. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Statistical Classifier to Support Diagnose Meningitis in Less Developed Areas of Brazil.

Science.gov (United States)

Lélis, Viviane-Maria; Guzmán, Eduardo; Belmonte, María-Victoria

2017-08-11

This paper describes the development of statistical classifiers to help diagnose meningococcal meningitis, i.e. the most sever, infectious and deadliest type of this disease. The goal is to find a mechanism able to determine whether a patient has this type of meningitis from a set of symptoms that can be directly observed in the earliest stages of this pathology. Currently, in Brazil, a country that is heavily affected by meningitis, all suspected cases require immediate hospitalization and the beginning of a treatment with invasive tests and medicines. This procedure, therefore, entails expensive treatments unaffordable in less developed regions. For this purpose, we have gathered together a dataset of 22,602 records of suspected meningitis cases from the Brazilian state of Bahia. Seven classification techniques have been applied from input data of nine symptoms and other information about the patient such as age, sex and the area they live in, and a 10 cross-fold validation has been performed. Results show that the techniques applied are suitable for diagnosing the meningococcal meningitis. Several indexes, such as precision, recall or ROC area, have been computed to show the accuracy of the models. All of them provide good results, but the best corresponds to the J48 classifier with a precision of 0.942 and a ROC area over 0.95. These results indicate that our model can indeed help lead to a non-invasive and early diagnosis of this pathology. This is especially useful in less developed areas, where the epidemiologic risk is usually high and medical expenses, sometimes, unaffordable.

Classifying regularized sensor covariance matrices: An alternative to CSP

NARCIS (Netherlands)

Roijendijk, L.M.M.; Gielen, C.C.A.M.; Farquhar, J.D.R.

2016-01-01

Common spatial patterns ( CSP) is a commonly used technique for classifying imagined movement type brain-computer interface ( BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline

Classifying regularised sensor covariance matrices: An alternative to CSP

NARCIS (Netherlands)

Roijendijk, L.M.M.; Gielen, C.C.A.M.; Farquhar, J.D.R.

2016-01-01

Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline

Using MODIS spectral information to classify sea ice scenes for CERES radiance-to-flux inversion

Science.gov (United States)

Corbett, J.; Su, W.; Liang, L.; Eitzen, Z.

2013-12-01

The Clouds and Earth's Radiant Energy System (CERES) instruments on NASA's Terra and Aqua satellites measure the shortwave (SW) radiance reflected from the Earth. In order to provide an estimate of the top-of-atmosphere reflected SW flux we need to know the anisotropy of the radiance reflected from the scene. Sea Ice scenes are particularly complex due to the wide range of surface conditions that comprise sea ice. For example, the anisotropy of snow-covered sea ice is quite different to that of sea ice with melt-ponds. To attempt to provide a consistent scene classification we have developed the Sea Ice Brightness Index (SIBI). The SIBI is defined as one minus the normalized difference between reflectances from the 0.469 micron and 0.858 micron bands from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. For brighter snow-covered sea ice scenes the SIBI value is close to 1.0. As the surface changes to bare ice, melt ponds, etc. the SIBI decreases. For open water the SIBI value is around 0.2-0.3. The SIBI exhibits no dependence on viewing zenith or solar zenith angle, allowing for consistent scene identification. To use the SIBI we classify clear-sky CERES field-of-views over sea ice into 3 groups; SIBI≥0.935, 0.935>SIBI≥0.85 and SIBISIBI based ADMs. Using the second metric, we see a reduction in the latitude/longitude binned mean RMS error between the ADM predicted radiance and the measured radiance from 8% to 7% in May and from 17% to 12% in July. These improvements suggest that using the SIBI to account for changes in the sea ice surface will lead to improved CERES flux retrievals.

Constrained parameter estimation for semi-supervised learning : The case of the nearest mean classifier

NARCIS (Netherlands)

Loog, M.

2011-01-01

A rather simple semi-supervised version of the equally simple nearest mean classifier is presented. However simple, the proposed approach is of practical interest as the nearest mean classifier remains a relevant tool in biomedical applications or other areas dealing with relatively high-dimensional

An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data

Directory of Open Access Journals (Sweden)

Datta Susmita

2010-08-01

Full Text Available Abstract Background Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show that our somewhat complex strategy has better predictive performance as judged on test samples than a more naive approach that attempts to directly identify the optimal classifier based on the training data performances of the individual classifiers. Results We illustrate the proposed method with two simulated and two real-data examples. In all cases, the ensemble classifier performs at the level of the best individual classifier comprising the ensemble or better. Conclusions For complex high-dimensional datasets resulting from present day high-throughput experiments, it may be wise to consider a number of classification algorithms combined with dimension reduction techniques rather than a fixed standard algorithm set a priori.

Gearbox Condition Monitoring Using Advanced Classifiers

Directory of Open Access Journals (Sweden)

P. Večeř

2010-01-01

Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing. 

A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

Directory of Open Access Journals (Sweden)

Marko Scholze

2010-03-01

Full Text Available Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN and Spectral Angle Mapper (SAM classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878 the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795. Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

Directory of Open Access Journals (Sweden)

Dawei Li

2017-01-01

Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

Evaluating the Performance of Multiple Classifier Systems: A Matrix Algebra Representation of Boolean Fusion Rules

National Research Council Canada - National Science Library

Hill, Justin

2003-01-01

...., a logical OR, AND, or a majority vote of the classifiers in the system). An established method for evaluating a classifier is measuring some aspect of its Receiver Operating Characteristic (ROC...

A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

Directory of Open Access Journals (Sweden)

Tao Geng

2008-01-01

Full Text Available A novel 4-class single-trial brain computer interface (BCI based on two (rather than four or more binary linear discriminant analysis (LDA classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

MAMMOGRAMS ANALYSIS USING SVM CLASSIFIER IN COMBINED TRANSFORMS DOMAIN

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

Zhongqin, G.; Chen, Y.

2017-12-01

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

Using Statistical Process Control Methods to Classify Pilot Mental Workloads

National Research Council Canada - National Science Library

Kudo, Terence

2001-01-01

.... These include cardiac, ocular, respiratory, and brain activity measures. The focus of this effort is to apply statistical process control methodology on different psychophysiological features in an attempt to classify pilot mental workload...

Word2Vec inversion and traditional text classifiers for phenotyping lupus.

Science.gov (United States)

Turner, Clayton A; Jacobs, Alexander D; Marques, Cassios K; Oates, James C; Kamen, Diane L; Anderson, Paul E; Obeid, Jihad S

2017-08-22

Identifying patients with certain clinical criteria based on manual chart review of doctors' notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR). This task can be automated using text classifiers based on Natural Language Processing (NLP) techniques along with pattern recognition machine learning (ML) algorithms. The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus (SLE) in comparison with a newer Bayesian word vector method. We obtained clinical notes for patients with SLE diagnosis along with controls from the Rheumatology Clinic (662 total patients). Sparse bag-of-words (BOWs) and Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) matrices were produced using NLP pipelines. These matrices were subjected to several different NLP classifiers: neural networks, random forests, naïve Bayes, support vector machines, and Word2Vec inversion, a Bayesian inversion method. Performance was measured by calculating accuracy and area under the Receiver Operating Characteristic (ROC) curve (AUC) of a cross-validated (CV) set and a separate testing set. We calculated the accuracy of the ICD-9 billing codes as a baseline to be 90.00% with an AUC of 0.900, the shallow neural network with CUIs to be 92.10% with an AUC of 0.970, the random forest with BOWs to be 95.25% with an AUC of 0.994, the random forest with CUIs to be 95.00% with an AUC of 0.979, and the Word2Vec inversion to be 90.03% with an AUC of 0.905. Our results suggest that a shallow neural network with CUIs and random forests with both CUIs and BOWs are the best classifiers for this lupus phenotyping task. The Word2Vec inversion method failed to significantly beat the ICD-9 code classification, but yielded promising results. This method does not require explicit features and is more adaptable to non-binary classification tasks. The Word2Vec inversion is

A neural-fuzzy approach to classify the ecological status in surface waters

International Nuclear Information System (INIS)

Ocampo-Duque, William; Schuhmacher, Marta; Domingo, Jose L.

2007-01-01

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive. - Fuzzy inference systems can be used as environmental classifiers

How well Can We Classify SWOT-derived Water Surface Profiles?

Science.gov (United States)

Frasson, R. P. M.; Wei, R.; Picamilh, C.; Durand, M. T.

2015-12-01

The upcoming Surface Water Ocean Topography (SWOT) mission will detect water bodies and measure water surface elevation throughout the globe. Within its continental high resolution mask, SWOT is expected to deliver measurements of river width, water elevation and slope of rivers wider than ~50 m. The definition of river reaches is an integral step of the computation of discharge based on SWOT's observables. As poorly defined reaches can negatively affect the accuracy of discharge estimations, we seek strategies to break up rivers into physically meaningful sections. In the present work, we investigate how accurately we can classify water surface profiles based on simulated SWOT observations. We assume that most river sections can be classified as either M1 (mild slope, with depth larger than the normal depth), or A1 (adverse slope with depth larger than the critical depth). This assumption allows the classification to be based solely on the second derivative of water surface profiles, with convex profiles being classified as A1 and concave profiles as M1. We consider a HEC-RAS model of the Sacramento River as a representation of the true state of the river. We employ the SWOT instrument simulator to generate a synthetic pass of the river, which includes our best estimates of height measurement noise and geolocation errors. We process the resulting point cloud of water surface heights with the RiverObs package, which delineates the river center line and draws the water surface profile. Next, we identify inflection points in the water surface profile and classify the sections between the inflection points. Finally, we compare our limited classification of simulated SWOT-derived water surface profile to the "exact" classification of the modeled Sacramento River. With this exercise, we expect to determine if SWOT observations can be used to find inflection points in water surface profiles, which would bring knowledge of flow regimes into the definition of river reaches.

Double Ramp Loss Based Reject Option Classifier

Science.gov (United States)

2015-05-22

of convex (DC) functions. To minimize it, we use DC programming approach [1]. The proposed method has following advantages: (1) the proposed loss LDR ...space constraints. We see that LDR does not put any restriction on ρ for it to be an upper bound of L0−d−1. 2.2 Risk Formulation Using LDR Let S = {(xn...classifier learnt using LDR based approach (C = 100, μ = 1, d = .2). Filled circles and triangles represent the support vectors. 4 Experimental Results We show

Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.

Science.gov (United States)

Fan, Jianping; Zhou, Ning; Peng, Jinye; Gao, Ling

2015-11-01

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.

Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers.

Science.gov (United States)

Siuly; Yin, Xiaoxia; Hadjiloucas, Sillas; Zhang, Yanchun

2016-04-01