WorldWideScience

Sample records for classified information

  1. 76 FR 34761 - Classified National Security Information

    Science.gov (United States)

    2011-06-14

    ... Classified National Security Information AGENCY: Marine Mammal Commission. ACTION: Notice. SUMMARY: This... information, as directed by Information Security Oversight Office regulations. FOR FURTHER INFORMATION CONTACT..., ``Classified National Security Information,'' and 32 CFR part 2001, ``Classified National Security......

  2. 75 FR 705 - Classified National Security Information

    Science.gov (United States)

    2010-01-05

    ... Executive Order 13526--Classified National Security Information Memorandum of December 29, 2009--Implementation of the Executive Order ``Classified National Security Information'' Order of December 29, 2009... ] Executive Order 13526 of December 29, 2009 Classified National Security Information This order prescribes...

  3. 15 CFR 4.8 - Classified Information.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily...

  4. 75 FR 37253 - Classified National Security Information

    Science.gov (United States)

    2010-06-28

    ... and Records Administration Information Security Oversight Office 32 CFR Parts 2001 and 2003 Classified National Security Information; Final Rule #0;#0;Federal Register / Vol. 75, No. 123 / Monday, June 28, 2010 / Rules and Regulations#0;#0; ] NATIONAL ARCHIVES AND RECORDS ADMINISTRATION Information...

  5. Searching and Classifying non-textual information

    OpenAIRE

    Arentz, Will Archer

    2004-01-01

    This dissertation contains a set of contributions that deal with search or classification of non-textual information. Each contribution can be considered a solution to a specific problem, in an attempt to map out a common ground. The problems cover a wide range of research fields, including search in music, classifying digitally sampled music, visualization and navigation in search results, and classifying images and Internet sites.On classification of digitally sample music, as method for ex...

  6. 3 CFR - Classified Information and Controlled Unclassified Information

    Science.gov (United States)

    2010-01-01

    ... 3 The President 1 2010-01-01 2010-01-01 false Classified Information and Controlled Unclassified Information Presidential Documents Other Presidential Documents Memorandum of May 27, 2009 Classified... and perceived technological obstacles to moving toward an information sharing culture, continue...

  7. Comparing cosmic web classifiers using information theory

    CERN Document Server

    Leclercq, Florent; Jasche, Jens; Wandelt, Benjamin

    2016-01-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-web, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  8. Comparing cosmic web classifiers using information theory

    Science.gov (United States)

    Leclercq, Florent; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin

    2016-08-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  9. Use Restricted - Classified information sharing, case NESA

    OpenAIRE

    El-Bash, Amira

    2015-01-01

    This Thesis is written for the Laurea University of Applied Sciences under the Bachelor’s Degree in Security Management. The empirical research of the thesis was supported by the National Emergency Supply Agency as a CASE study, in classified information sharing in the organization. The National Emergency Supply Agency was chosen for the research because of its social significance and distinctively wide operation field. Being one of the country’s administrator’s actors, its range of tasks in ...

  10. Comparing cosmic web classifiers using information theory

    OpenAIRE

    Leclercq, Florent; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin

    2016-01-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative perf...

  11. 10 CFR 110.126 - Protection of classified information.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 2 2010-01-01 2010-01-01 false Protection of classified information. 110.126 Section 110... MATERIAL Special Procedures for Classified Information in Hearings § 110.126 Protection of classified information. Nothing in this subpart shall relieve any person from safeguarding classified information...

  12. Use of information barriers to protect classified information

    International Nuclear Information System (INIS)

    This paper discusses the detailed requirements for an information barrier (IB) for use with verification systems that employ intrusive measurement technologies. The IB would protect classified information in a bilateral or multilateral inspection of classified fissile material. Such a barrier must strike a balance between providing the inspecting party the confidence necessary to accept the measurement while protecting the inspected party's classified information. The authors discuss the structure required of an IB as well as the implications of the IB on detector system maintenance. A defense-in-depth approach is proposed which would provide assurance to the inspected party that all sensitive information is protected and to the inspecting party that the measurements are being performed as expected. The barrier could include elements of physical protection (such as locks, surveillance systems, and tamper indicators), hardening of key hardware components, assurance of capabilities and limitations of hardware and software systems, administrative controls, validation and verification of the systems, and error detection and resolution. Finally, an unclassified interface could be used to display and, possibly, record measurement results. The introduction of an IB into an analysis system may result in many otherwise innocuous components (detectors, analyzers, etc.) becoming classified and unavailable for routine maintenance by uncleared personnel. System maintenance and updating will be significantly simplified if the classification status of as many components as possible can be made reversible (i.e. the component can become unclassified following the removal of classified objects)

  13. What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers?

    CERN Document Server

    Hu, Bao-Gang

    2011-01-01

    In this study, both Bayesian classifiers and mutual information classifiers are examined for binary classifications with or without a reject option. The general decision rules in terms of distinctions on error types and reject types are derived for Bayesian classifiers. A formal analysis is conducted to reveal the parameter redundancy of cost terms when abstaining classifications are enforced. The redundancy implies an intrinsic problem of "non-consistency" for interpreting cost terms. If no data is given to the cost terms, we demonstrate the weakness of Bayesian classifiers in class-imbalanced classifications. On the contrary, mutual-information classifiers are able to provide an objective solution from the given data, which shows a reasonable balance among error types and reject types. Numerical examples of using two types of classifiers are given for confirming the theoretical differences, including the extremely-class-imbalanced cases. Finally, we briefly summarize the Bayesian classifiers and mutual-info...

  14. 6 CFR 7.23 - Emergency release of classified information.

    Science.gov (United States)

    2010-01-01

    ... Classified Information Non-disclosure Form. In emergency situations requiring immediate verbal release of... information through approved communication channels by the most secure and expeditious method possible, or...

  15. What are the differences between Bayesian classifiers and mutual-information classifiers?

    Science.gov (United States)

    Hu, Bao-Gang

    2014-02-01

    In this paper, both Bayesian and mutual-information classifiers are examined for binary classifications with or without a reject option. The general decision rules are derived for Bayesian classifiers with distinctions on error types and reject types. A formal analysis is conducted to reveal the parameter redundancy of cost terms when abstaining classifications are enforced. The redundancy implies an intrinsic problem of nonconsistency for interpreting cost terms. If no data are given to the cost terms, we demonstrate the weakness of Bayesian classifiers in class-imbalanced classifications. On the contrary, mutual-information classifiers are able to provide an objective solution from the given data, which shows a reasonable balance among error types and reject types. Numerical examples of using two types of classifiers are given for confirming the differences, including the extremely class-imbalanced cases. Finally, we briefly summarize the Bayesian and mutual-information classifiers in terms of their application advantages and disadvantages, respectively. PMID:24807026

  16. 32 CFR 2400.30 - Reproduction of classified information.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Reproduction of classified information. 2400.30... SECURITY PROGRAM Safeguarding § 2400.30 Reproduction of classified information. Documents or portions of... the originator or higher authority. Any stated prohibition against reproduction shall be...

  17. Classifying and identifying servers for biomedical information retrieval.

    OpenAIRE

    Patrick, T. B.; Springer, G K

    1994-01-01

    Useful retrieval of biomedical information from network information sources requires methods for organized access to those information sources. This access must be organized in terms of the information content of information sources and in terms of the discovery of the network location of those information sources. We have developed an approach to providing organized access to information sources based on a scheme of hierarchical classifiers and identifiers of the servers providing access to ...

  18. Using linguistic information to classify Portuguese text documents

    OpenAIRE

    Teresa, Gonçalves; Paulo, Quaresma

    2008-01-01

    This paper examines the role of various linguistic structures on text classification applying the study to the Portuguese language. Besides using a bag-of-words representation where we evaluate different measures and use linguistic knowledge for term selection, we do several experiments using syntactic information representing documents as strings of words and strings of syntactic parse trees. To build the classifier we use the Support Vector Machine (SVM) algorithm which is known to prod...

  19. An Informed Framework for Training Classifiers from Social Media

    OpenAIRE

    Dong Seon Cheng; Sami Abduljalil Abdulhak

    2016-01-01

    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 som...

  20. 46 CFR 503.59 - Safeguarding classified information.

    Science.gov (United States)

    2010-10-01

    ... maintain: (1) A classified document register or log containing a listing of all classified holdings, and (2) A classified document destruction register or log containing the title and date of all classified... documents. (m) Combinations to dial-type locks shall be changed only by persons having an...

  1. 75 FR 51609 - Classified National Security Information Program for State, Local, Tribal, and Private Sector...

    Science.gov (United States)

    2010-08-23

    ... National Security Information Program for State, Local, Tribal, and Private Sector Entities By the... established a Classified National Security Information Program (Program) designed to safeguard and govern access to classified national security information shared by the Federal Government with State,...

  2. Research on Classified Protection-based Security Construction for University Information Systems

    OpenAIRE

    Chunling Wu; Hehua Li; Wei Wei

    2013-01-01

    Information security classified protection is a basic system of Chinas information security protection. Conducting information security classified protection in colleges and universities is not only a key content in strengthening national information security work, but also an effective measure to improve the information security level of university networks. The paper first summarizes common information subsystems in Chinas universities and colleges,...

  3. 32 CFR 2004.21 - Protection of Classified Information [201(e)].

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Protection of Classified Information . 2004.21 Section 2004.21 National Defense Other Regulations Relating to National Defense INFORMATION SECURITY... DIRECTIVE NO. 1 Operations § 2004.21 Protection of Classified Information . Procedures for the...

  4. 48 CFR 8.608 - Protection of classified and sensitive information.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Protection of classified and sensitive information. 8.608 Section 8.608 Federal Acquisition Regulations System FEDERAL... Prison Industries, Inc. 8.608 Protection of classified and sensitive information. Agencies shall...

  5. 14 CFR 1213.106 - Preventing release of classified information to the media.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Preventing release of classified... ADMINISTRATION RELEASE OF INFORMATION TO NEWS AND INFORMATION MEDIA § 1213.106 Preventing release of classified... employee from responsibility for preventing any unauthorized release. See NPR 1600.1, Chapter 5, Section...

  6. Classifying and Designing the Educational Methods with Information Communications Technoligies

    Directory of Open Access Journals (Sweden)

    I. N. Semenova

    2015-03-01

    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. 

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Paun J. Bereš

    2014-04-01

    Full Text Available 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 confidential information in order to inform them of our national system of protection of classified information and enable the implementation of the existing legislation applying the  heuristic model of education. This article describes the legal regulations governing the protection of dataand shows mandatory standards and measures for the protection of classified information.

  9. A Probabilistic Approach to Classifying Supernovae Using Photometric Information

    OpenAIRE

    Natalia V. Kuznetsova; Connolly, Brian M.

    2006-01-01

    This paper presents a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, \\emph{etc.}), using its photometric information alone. It is validated with Monte Carlo, and both space- and ground- based data. We examine the application of the method to well-sampled as well as poorly sampled supernova light curves and investigate to what extent the best currently available supernova models can be used for typing supernova c...

  10. A Probabilistic Approach to Classifying Supernovae Using Photometric Information

    CERN Document Server

    Kuznetsova, N V; Kuznetsova, Natalia V.; Connolly, Brian M.

    2006-01-01

    This paper presents a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, \\emph{etc.}), using its photometric information alone. It is validated with Monte Carlo, and both space- and ground- based data. We examine the application of the method to well-sampled as well as poorly sampled supernova light curves. Central to the method is the assumption that a supernova candidate belongs to a group of objects that can be modeled; we therefore discuss possible ways of removing anomalous or less well understood events from the sample. This method is particularly advantageous for analyses where the purity of the supernova sample is of the essence, or for those where it is important to know the number of the supernova candidates of a certain type (\\emph{e.g.}, in supernova rate studies).

  11. 75 FR 733 - Implementation of the Executive Order, ``Classified National Security Information''

    Science.gov (United States)

    2010-01-05

    ... National Security Information'' Memorandum for the Heads of Executive Departments and Agencies Today I have signed an executive order entitled, ``Classified National Security Information'' (the ``order''), which... Director of the Information Security Oversight Office (ISOO) a copy of the department or agency...

  12. An Evaluation of Information Criteria Use for Correct Cross-Classified Random Effects Model Selection

    Science.gov (United States)

    Beretvas, S. Natasha; Murphy, Daniel L.

    2013-01-01

    The authors assessed correct model identification rates of Akaike's information criterion (AIC), corrected criterion (AICC), consistent AIC (CAIC), Hannon and Quinn's information criterion (HQIC), and Bayesian information criterion (BIC) for selecting among cross-classified random effects models. Performance of default values for the 5…

  13. Local Sequence Information-based Support Vector Machine to Classify Voltage-gated Potassium Channels

    Institute of Scientific and Technical Information of China (English)

    Li-Xia LIU; Meng-Long LI; Fu-Yuan TAN; Min-Chun LU; Ke-Long WANG; Yan-Zhi GUO; Zhi-Ning WEN; Lin JIANG

    2006-01-01

    In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP); reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.

  14. 28 CFR 17.47 - Denial or revocation of eligibility for access to classified information.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Denial or revocation of eligibility for access to classified information. 17.47 Section 17.47 Judicial Administration DEPARTMENT OF JUSTICE..., the identity of the deciding authority, and written notice of the right to appeal. (d) Within 30...

  15. 3 CFR 13526 - Executive Order 13526 of December 29, 2009. Classified National Security Information

    Science.gov (United States)

    2010-01-01

    ... documents in the physical and legal custody of the National Archives and Records Administration (National... classification standards and routine, secure, and effective declassification are equally important priorities... CLASSIFICATION Section 1.1. Classification Standards. (a) Information may be originally classified under...

  16. Attribute verification systems with information barriers for classified forms of plutonium in the trilateral initiative

    International Nuclear Information System (INIS)

    A team of technical experts from the Russian Federation, the International Atomic Energy Agency (IAEA), and the United States has been working since December 1997 to develop a toolkit 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. However, this equipment is very intrusive, 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 safeguards technologies. In this paper, these new technologies will be described. (author)

  17. Recognition of medication information from discharge summaries using ensembles of classifiers

    Directory of Open Access Journals (Sweden)

    Doan Son

    2012-05-01

    Full Text Available Abstract Background Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP. Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. Methods We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM based system, and a conditional random field (CRF based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting. Results Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall by the first-ranked system in the challenge. Conclusions Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that

  18. Recognition of medication information from discharge summaries using ensembles of classifiers

    OpenAIRE

    Doan Son; Collier Nigel; Xu Hua; Duy Pham; Phuong Tu

    2012-01-01

    Abstract Background Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has n...

  19. A cascade of classifiers for extracting medication information from discharge summaries

    OpenAIRE

    Halgrim Scott; Xia Fei; Solti Imre; Cadag Eithon; Uzuner Özlem

    2011-01-01

    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 ...

  20. Classifying and filtering blind feedback terms to improve information retrieval effectiveness

    OpenAIRE

    Leveling, Johannes; Jones, Gareth J. F.

    2010-01-01

    The classification of blind relevance feedback (BRF) terms described in this paper aims at increasing precision or recall by determining which terms decrease, increase or do not change the corresponding information retrieval (IR) performance metric. Classification and IR experiments are performed on the German and English GIRT data, using the BM25 retrieval model. Several basic memory-based classifiers are trained on dierent feature sets, grouping together features from different query ...

  1. Detecting and Classifying Android Malware Using Static Analysis along with Creator Information

    OpenAIRE

    Hyunjae Kang; Jae-wook Jang; Aziz Mohaisen; Huy Kang Kim

    2015-01-01

    Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional underground actors; however previous studies overlooked such information as a feature in detecting and classifying malware and in attributing malware to creators. Guided by this insight, we propose a method to improve the performance of Android malware detection by incorporating the creat...

  2. Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers.

    Science.gov (United States)

    Xu, Minpeng; Liu, Jing; Chen, Long; Qi, Hongzhi; He, Feng; Zhou, Peng; Wan, Baikun; Ming, Dong

    2016-05-01

    Although the inter-subject information has been demonstrated to be effective for a rapid calibration of the P300-based brain-computer interface (BCI), it has never been comprehensively tested to find if the incorporation of heterogeneous data could enhance the accuracy. This study aims to improve the subject-specific P300 classifier by adding other subject's data. A classifier calibration strategy, weighted ensemble learning generic information (WELGI), was developed, in which elementary classifiers were constructed by using both the intra- and inter-subject information and then integrated into a strong classifier with a weight assessment. 55 subjects were recruited to spell 20 characters offline using the conventional P300-based BCI, i.e. the P300-speller. Four different metrics, the P300 accuracy and precision, the round accuracy, and the character accuracy, were performed for a comprehensive investigation. The results revealed that the classifier constructed on the training dataset in combination with adding other subject's data was significantly superior to that without the inter-subject information. Therefore, the WELGI is an effective classifier calibration strategy which uses the inter-subject information to improve the accuracy of subject-specific P300 classifiers, and could also be applied to other BCI paradigms. PMID:27005002

  3. Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor.

    Science.gov (United States)

    Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang

    2016-01-01

    In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider's hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse's gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider's motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country's top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098

  4. Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor

    Science.gov (United States)

    Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang

    2016-01-01

    In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098

  5. 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)),...

  6. A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination

    Science.gov (United States)

    Tan, Kun; Hu, Jun; Li, Jun; Du, Peijun

    2015-07-01

    In the process of semi-supervised hyperspectral image classification, spatial neighborhood information of training samples is widely applied to solve the small sample size problem. However, the neighborhood information of unlabeled samples is usually ignored. In this paper, we propose a new algorithm for hyperspectral image semi-supervised classification in which the spatial neighborhood information is combined with classifier to enhance the classification ability in determining the class label of the selected unlabeled samples. There are two key points in this algorithm: (1) it is considered that the correct label should appear in the spatial neighborhood of unlabeled samples; (2) the combination of classifier can obtains better results. Two classifiers multinomial logistic regression (MLR) and k-nearest neighbor (KNN) are combined together in the above way to further improve the performance. The performance of the proposed approach was assessed with two real hyperspectral data sets, and the obtained results indicate that the proposed approach is effective for hyperspectral classification.

  7. 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....

  8. Towards Evidence-based Precision Medicine: Extracting Population Information from Biomedical Text using Binary Classifiers and Syntactic Patterns.

    Science.gov (United States)

    Raja, Kalpana; Dasot, Naman; Goyal, Pawan; Jonnalagadda, Siddhartha R

    2016-01-01

    Precision Medicine is an emerging approach for prevention and treatment of disease that considers individual variability in genes, environment, and lifestyle for each person. The dissemination of individualized evidence by automatically identifying population information in literature is a key for evidence-based precision medicine at the point-of-care. We propose a hybrid approach using natural language processing techniques to automatically extract the population information from biomedical literature. Our approach first implements a binary classifier to classify sentences with or without population information. A rule-based system based on syntactic-tree regular expressions is then applied to sentences containing population information to extract the population named entities. The proposed two-stage approach achieved an F-score of 0.81 using a MaxEnt classifier and the rule- based system, and an F-score of 0.87 using a Nai've-Bayes classifier and the rule-based system, and performed relatively well compared to many existing systems. The system and evaluation dataset is being released as open source. PMID:27570671

  9. 高校信息安全等级保护评测%University classified protection of information security evaluation

    Institute of Scientific and Technical Information of China (English)

    刘玉燕

    2011-01-01

    Information security classified protection, risk assessment, system security assessment is the current state of the construction of information security system is an important content. Rank is standard,evaluation is the means. With everyone here is to protect infonnation security classified related issues.Implementation of infonnation security classified protection can promote network security service to establish and perfect the mechanism, to adopt systems, standardized, scientific management and safeguard measures, to improve technology classified of information security and protection, security departments business system efficiency, safety operation.%信息安全等级保护、风险评估、系统安全测评是当前国家信息安全保障体系建设的重要内容.等级保护是标准,评估、测评是手段.这里探讨的是与信息安全等级保护相关问题.实施信息安全等级保护可以推动网络安全服务机制的建立和完善;有利于采取系统、规范、科学的管理和技术保障措施,提高信息安全保护水平;保障各部门业务系统高效、安全运转.

  10. 36 CFR 1256.74 - How does NARA process Freedom of Information Act (FOIA) requests for classified information?

    Science.gov (United States)

    2010-07-01

    ... accordance with the provisions of 36 CFR part 1250. Time limits for responses to FOIA requests for classified... CFR, 1995 Comp., p. 333), as amended by Executive Order 13292 (68 FR 15315, 3 CFR, 2003 Comp., p. 196... FOIA, the PRA, and Executive Order 13233, Further Implementation of the Presidential Records Act (3...

  11. 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... permitted by the Commission when it determines that the public interest will not be prejudiced. (c)...

  12. MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information.

    Science.gov (United States)

    Pai, Priyadarshini P; Mondal, Sukanta

    2016-10-01

    Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein-mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development. PMID:26457920

  13. 77 FR 65709 - Agency Information Collection Activities: Petition To Classify Orphan as an Immediate Relative...

    Science.gov (United States)

    2012-10-30

    ... call the USCIS National Customer Service Center at 1-800-375-5283. Written comments and suggestions... adult member (age 18 and older), who lives in the home of the prospective adoptive parent(s), except for... SECURITY U.S. Citizenship and Immigration Services Agency Information Collection Activities: Petition...

  14. Supervised Feature Subset Selection based on Modified Fuzzy Relative Information Measure for classifier Cart

    OpenAIRE

    K.SAROJINI,; Dr. K.THANGAVEL; D.DEVAKUMARI

    2010-01-01

    Feature subset selection is an essential task in data mining. This paper presents a new method for dealing with supervised feature subset selection based on Modified Fuzzy Relative Information Measure (MFRIM). First, Discretization algorithm is applied to discretize numeric features to construct the membership functions of each fuzzy sets of a feature. Then the proposed MFRIM is applied to select the feature subset focusing on boundary samples. The proposed method can select feature subset wi...

  15. 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...

  16. Supervised Feature Subset Selection based on Modified Fuzzy Relative Information Measure for classifier Cart

    Directory of Open Access Journals (Sweden)

    K.SAROJINI,

    2010-06-01

    Full Text Available Feature subset selection is an essential task in data mining. This paper presents a new method for dealing with supervised feature subset selection based on Modified Fuzzy Relative Information Measure (MFRIM. First, Discretization algorithm is applied to discretize numeric features to construct the membership functions of each fuzzy sets of a feature. Then the proposed MFRIM is applied to select the feature subset focusing on boundary samples. The proposed method can select feature subset with minimum number of features, which are relevant to get higher average classification accuracy for datasets. The experimental results with UCI datasets show that the proposed algorithm is effective and efficient in selecting subset with minimum number of features getting higher average classification accuracy than the consistency based feature subset selection method.

  17. 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…

  18. Classifying Microorganisms.

    Science.gov (United States)

    Baker, William P.; Leyva, Kathryn J.; Lang, Michael; Goodmanis, Ben

    2002-01-01

    Focuses on an activity in which students sample air at school and generate ideas about how to classify the microorganisms they observe. The results are used to compare air quality among schools via the Internet. Supports the development of scientific inquiry and technology skills. (DDR)

  19. 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...... corporations construing themselves as able and suitable to manage their emissions, and, additionally, given that the construction of carbon emissions has performative consequences, the underlying practices need to be declassified, i.e. opened for public scrutiny. Hence the paper concludes by arguing for a...

  20. Multi-source Fuzzy Information Fusion Method Based on Bayesian Optimal Classifier%基于贝叶斯最优分类器的多源模糊信息融合方法

    Institute of Scientific and Technical Information of China (English)

    苏宏升

    2008-01-01

    To make conventional Bayesian optimal classifier possess the abilities of disposing fuzzy information and realizing the automation of reasoning process, a new Bayesian optimal classifier is proposed with fuzzy information embedded. It can not only dispose fuzzy information effectively, but also retain learning properties of Bayesian optimal classifier. In addition, according to the evolution of fuzzy set theory, vague set is also imbedded into it to generate vague Bayesian optimal classifier. It can simultaneously simulate the twofold characteristics of fuzzy information from the positive and reverse directions. Further, a set pair Bayesian optimal classifier is also proposed considering the threefold characteristics of fuzzy information from the positive, reverse, and indeterminate sides. In the end, a knowledge-based artificial neural network (KBANN) is presented to realize automatic reasoning of Bayesian optimal classifier. It not only reduces the computational cost of Bayesian optimal classifier but also improves its classification learning quality.

  1. 基于层次分析涉密信息系统风险评估%Classified Information System Security Risk Assessment based on Hierarchical Analysis

    Institute of Scientific and Technical Information of China (English)

    李增鹏; 马春光; 李迎涛

    2014-01-01

    信息技术的发展使得政府和军队相关部门对信息系统安全问题提出更高要求。涉密信息系统安全有其独特性,风险评估区别于普通信息安全系统。文章以涉密信息系统为研究对象,首先阐述涉密信息系统的特点,针对其独特性对现有信息安全风险评估方法进行分析评价。然后将基于层析分析法的评估模型引入到涉密信息系统安全风险评估中。为涉密信息系统进行风险评估提供一种新的技术思路。最后通过实例分析,所提模型在处理涉密信息系统评估过程中对得到的离散数据分布无要求,与德菲尔法以及 BP 神经网络相比,该模型具有一定实用性和可扩张性,适合用于实际地涉密信息系统风险评估中。%The development of information technology makes the government and military authorities put forward higher requirements on the security of information system. The uniqueness of classified information system security makes risk assessment different from the common information security system. In this paper, classified information systems are research object. We first describe the characteristics of information system security, the uniqueness of the existing methods of analysis and evaluation of information security risk assessment. Then an evaluation model based on AHP is introduced into security risk assessment of information system security. A new way of classified information system risk assessment is presented. At last, through the analysis of an example, we analyze that the proposed model in processing of information system security evaluation process to get the discrete data distribution is not required, compared with the Delphi method and BP neural network, this model is highly of practicability and expansibility, it does suitable for classified information system risk assessment actually.

  2. Classified Catalogue Code of Ranganathan: A Proposal to Make it Compatible for Developing Compute~Based Library Information Systems

    OpenAIRE

    Madan Mohan Kashyap

    2001-01-01

    This paper deals with the differences between the environments of card catalogue and online catalogue, and emphasises on the need of developing computer-based library information systems and services. It describes database technology, kinds of databases, database management system, computerised library information system, and management information system. It coven in detail the database design and compatibility of cataloguing codes for developing databases of computer-based library informati...

  3. A Classifier Ensemble of Binary Classifier Ensembles

    Directory of Open Access Journals (Sweden)

    Sajad Parvin

    2011-09-01

    Full Text Available This paper proposes an innovative combinational algorithm to improve the performance in multiclass classification domains. Because the more accurate classifier the better performance of classification, the researchers in computer communities have been tended to improve the accuracies of classifiers. Although a better performance for classifier is defined the more accurate classifier, but turning to the best classifier is not always the best option to obtain the best quality in classification. It means to reach the best classification there is another alternative to use many inaccurate or weak classifiers each of them is specialized for a sub-space in the problem space and using their consensus vote as the final classifier. So this paper proposes a heuristic classifier ensemble to improve the performance of classification learning. It is specially deal with multiclass problems which their aim is to learn the boundaries of each class from many other classes. Based on the concept of multiclass problems classifiers are divided into two different categories: pairwise classifiers and multiclass classifiers. The aim of a pairwise classifier is to separate one class from another one. Because of pairwise classifiers just train for discrimination between two classes, decision boundaries of them are simpler and more effective than those of multiclass classifiers.The main idea behind the proposed method is to focus classifier in the erroneous spaces of problem and use of pairwise classification concept instead of multiclass classification concept. Indeed although usage of pairwise classification concept instead of multiclass classification concept is not new, we propose a new pairwise classifier ensemble with a very lower order. In this paper, first the most confused classes are determined and then some ensembles of classifiers are created. The classifiers of each of these ensembles jointly work using majority weighting votes. The results of these ensembles

  4. Classifying Pediatric Central Nervous System Tumors through near Optimal Feature Selection and Mutual Information: A Single Center Cohort

    Directory of Open Access Journals (Sweden)

    Mohammad Faranoush

    2013-10-01

    Full Text Available Background: Labeling, gathering mutual information, clustering and classificationof central nervous system tumors may assist in predicting not only distinct diagnosesbased on tumor-specific features but also prognosis. This study evaluates the epidemi-ological features of central nervous system tumors in children who referred to Mahak’sPediatric Cancer Treatment and Research Center in Tehran, Iran.Methods: This cohort (convenience sample study comprised 198 children (≤15years old with central nervous system tumors who referred to Mahak's PediatricCancer Treatment and Research Center from 2007 to 2010. In addition to the descriptiveanalyses on epidemiological features and mutual information, we used the LeastSquares Support Vector Machines method in MATLAB software to propose apreliminary predictive model of pediatric central nervous system tumor feature-labelanalysis. Results:Of patients, there were 63.1% males and 36.9% females. Patients' mean±SDage was 6.11±3.65 years. Tumor location was as follows: supra-tentorial (30.3%, infra-tentorial (67.7% and 2% (spinal. The most frequent tumors registered were: high-gradeglioma (supra-tentorial in 36 (59.99% patients and medulloblastoma (infra-tentorialin 65 (48.51% patients. The most prevalent clinical findings included vomiting,headache and impaired vision. Gender, age, ethnicity, tumor stage and the presence ofmetastasis were the features predictive of supra-tentorial tumor histology.Conclusion: Our data agreed with previous reports on the epidemiology of centralnervous system tumors. Our feature-label analysis has shown how presenting features maypartially predict diagnosis. Timely diagnosis and management of central nervous systemtumors can lead to decreased disease burden and improved survival. This may be furtherfacilitated through development of partitioning, risk prediction and prognostic models.

  5. CLASSIFIER IN BODO

    OpenAIRE

    Pratima Brahma

    2014-01-01

    The present paper investigates the classifiers in Bodo. In Bodo classifiers have function as specific determiner of the physical shape or size, quantity and quality of the noun. Classifiers in Bodo are predominantly of monosyllabic structure. It occurs with numeral and the classifiers precede numeral. The monosyllabic structure may be single verb or simple verb and noun; it functions as classifiers by suffixing numerals. In Bodo, classifier can occur before and after in no...

  6. An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms

    International Nuclear Information System (INIS)

    Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC=0.905±0.024) in performance as compared to the original IT-CAD system (AUC=0.865±0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.

  7. An Informal Summary of a New Formalism for Classifying Spin-Orbit Systems Using Tools Distilled from the Theory of Bundles

    CERN Document Server

    Heinemann, Klaus; Ellison, James A; Vogt, Mathias

    2015-01-01

    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.

  8. 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

  9. Efficient information theoretic strategies for classifier combination, feature extraction and performance evaluation in improving false positives and false negatives for spam e-mail filtering.

    Science.gov (United States)

    Zorkadis, V; Karras, D A; Panayotou, M

    2005-01-01

    Spam emails are considered as a serious privacy-related violation, besides being a costly, unsolicited communication. Various spam filtering techniques have been so far proposed, mainly based on Naïve Bayesian algorithms. Other Machine Learning algorithms like Boosting trees, or Support Vector Machines (SVM) have already been used with success. However, the number of False Positives (FP) and False Negatives (FN) resulting through applying various spam e-mail filters still remains too high and the problem of spam e-mail categorization cannot be solved completely from a practical viewpoint. In this paper, we propose a novel approach for spam e-mail filtering based on efficient information theoretic techniques for integrating classifiers, for extracting improved features and for properly evaluating categorization accuracy in terms of FP and FN. The goal of the presented methodology is to empirically but explicitly minimize these FP and FN numbers by combining high-performance FP filters with high-performance FN filters emerging from a previous work of the authors [Zorkadis, V., Panayotou, M., & Karras, D. A. (2005). Improved spam e-mail filtering based on committee machines and information theoretic feature extraction. Proceedings of the International Joint Conference on Neural Networks, July 31-August 4, 2005, Montreal, Canada]. To this end, Random Committee-based filters along with ADTree-based ones are efficiently combined through information theory, respectively. The experiments conducted are of the most extensive ones so far in the literature, exploiting widely accepted benchmarking e-mail data sets and comparing the proposed methodology with the Naive Bayes spam filter as well as with the Boosting tree methodology, the classification via regression and other machine learning models. It is illustrated by means of novel information theoretic measures of FP & FN filtering performance that the proposed approach is very favorably compared to the other rival methods

  10. Classifying unstructured text using structured training instances and ensemble classifiers

    OpenAIRE

    Lianos, Andreas; Yang, Yanyan

    2015-01-01

    Typical supervised classification techniques require training instances similar to the values that need to be classified. This research proposes a methodology that can utilize training instances found in a different format. The benefit of this approach is that it allows the use of traditional classification techniques, without the need to hand-tag training instances if the information exists in other data sources. The proposed approach is presented through a practical classification applicati...

  11. Recognition Using Hybrid Classifiers.

    Science.gov (United States)

    Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

    2016-04-01

    A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply. PMID:26959677

  12. Dynamic system classifier

    CERN Document Server

    Pumpe, Daniel; Müller, Ewald; Enßlin, Torsten A

    2016-01-01

    Stochastic differential equations describe well many physical, biological and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of DSC to oscillation processes with a time dependent frequency {\\omega}(t) and damping factor {\\gamma}(t). Although real systems might be more complex, this simple oscillator captures many characteristic features. The {\\omega} and {\\gamma} timelines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiment...

  13. 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...

  14. Classifying Cereal Data

    Science.gov (United States)

    The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

  15. 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.

  16. Intelligent Garbage Classifier

    OpenAIRE

    Ignacio Rodríguez Novelle; Javier Pérez Cid; Alvaro Salmador

    2008-01-01

    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.

  17. Classifier in Age classification

    OpenAIRE

    B. Santhi; R.Seethalakshmi

    2012-01-01

    Face is the important feature of the human beings. We can derive various properties of a human by analyzing the face. The objective of the study is to design a classifier for age using facial images. Age classification is essential in many applications like crime detection, employment and face detection. The proposed algorithm contains four phases: preprocessing, feature extraction, feature selection and classification. The classification employs two class labels namely child and Old. This st...

  18. ANALYSIS OF BAYESIAN CLASSIFIER ACCURACY

    Directory of Open Access Journals (Sweden)

    Felipe Schneider Costa

    2013-01-01

    Full Text Available The naïve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naïve assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables, the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naïve Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naïve Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.

  19. Classified Stable Matching

    CERN Document Server

    Huang, Chien-Chung

    2009-01-01

    We introduce the {\\sc classified stable matching} problem, a problem motivated by academic hiring. Suppose that a number of institutes are hiring faculty members from a pool of applicants. Both institutes and applicants have preferences over the other side. An institute classifies the applicants based on their research areas (or any other criterion), and, for each class, it sets a lower bound and an upper bound on the number of applicants it would hire in that class. The objective is to find a stable matching from which no group of participants has reason to deviate. Moreover, the matching should respect the upper/lower bounds of the classes. In the first part of the paper, we study classified stable matching problems whose classifications belong to a fixed set of ``order types.'' We show that if the set consists entirely of downward forests, there is a polynomial-time algorithm; otherwise, it is NP-complete to decide the existence of a stable matching. In the second part, we investigate the problem using a p...

  20. Adaboost Ensemble Classifiers for Corporate Default Prediction

    Directory of Open Access Journals (Sweden)

    Suresh Ramakrishnan

    2015-01-01

    Full Text Available This study aims to show a substitute technique to corporate default prediction. Data mining techniques have been extensively applied for this task, due to its ability to notice non-linear relationships and show a good performance in presence of noisy information, as it usually happens in corporate default prediction problems. In spite of several progressive methods that have widely been proposed, this area of research is not out dated and still needs further examination. In this study, the performance of multiple classifier systems is assessed in terms of their capability to appropriately classify default and non-default Malaysian firms listed in Bursa Malaysia. Multi-stage combination classifiers provided significant improvements over the single classifiers. In addition, Adaboost shows improvement in performance over the single classifiers.

  1. Quality Classifiers for Open Source Software Repositories

    OpenAIRE

    Tsatsaronis, George; Halkidi, Maria; Giakoumakis, Emmanouel A.

    2009-01-01

    Open Source Software (OSS) often relies on large repositories, like SourceForge, for initial incubation. The OSS repositories offer a large variety of meta-data providing interesting information about projects and their success. In this paper we propose a data mining approach for training classifiers on the OSS meta-data provided by such data repositories. The classifiers learn to predict the successful continuation of an OSS project. The `successfulness' of projects is defined in terms of th...

  2. Evolving Classifiers: Methods for Incremental Learning

    OpenAIRE

    Hulley, Greg; Marwala, Tshilidzi

    2007-01-01

    The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic ...

  3. Property Accounting. A Handbook of Standard Terminology and a Guide for Classifying Information about Education Property. State Educational Records and Reports Series: Handbook III, Revised 1977.

    Science.gov (United States)

    Seibert, Ivan N.

    This handbook is offered as a resource for local, intermediate, state, and federal education officials to assist in the identification, organization, and definition of data and information about education property. An introduction and directions are followed by a discussion of concepts associated with property accounting and some general…

  4. Local Component Analysis for Nonparametric Bayes Classifier

    CERN Document Server

    Khademi, Mahmoud; safayani, Meharn

    2010-01-01

    The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...

  5. Classifying Entropy Measures

    Directory of Open Access Journals (Sweden)

    Angel Garrido

    2011-07-01

    Full Text Available Our paper analyzes some aspects of Uncertainty Measures. We need to obtain new ways to model adequate conditions or restrictions, constructed from vague pieces of information. The classical entropy measure originates from scientific fields; more specifically, from Statistical Physics and Thermodynamics. With time it was adapted by Claude Shannon, creating the current expanding Information Theory. However, the Hungarian mathematician, Alfred Rényi, proves that different and valid entropy measures exist in accordance with the purpose and/or need of application. Accordingly, it is essential to clarify the different types of measures and their mutual relationships. For these reasons, we attempt here to obtain an adequate revision of such fuzzy entropy measures from a mathematical point of view.

  6. Comparative Analysis of Classifier Fusers

    Directory of Open Access Journals (Sweden)

    Marcin Zmyslony

    2012-06-01

    Full Text Available There are many methods of decision making by an ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers’ outputs. This work presents comparative analysis of some classifier fusion methods based on weighted voting of classifiers’ responses and combination of classifiers’ discriminant functions. We discus different methods of producing combined classifiers based on weights. We show that it is notpossible to obtain classifier better than an abstract model of committee known as an Oracle if it is based only on weighted voting but models based on discriminant function or classifier using feature values and class numbers could outperform the Oracle as well. Delivered conclusions are confirmed by the results of computer experiments carried out on benchmark and computer generated data.

  7. Comparative Analysis of Classifier Fusers

    Directory of Open Access Journals (Sweden)

    Marcin Zmyslony

    2012-05-01

    Full Text Available There are many methods of decision making by an ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers’ outputs. This work presents comparative analysis of some classifier fusion methods based on weighted voting of classifiers’ responses and combination of classifiers’ discriminant functions. We discus different methods of producing combined classifiers based on weights. We show that it is not possible to obtain classifier better than an abstract model of committee known as an Oracle if it is based only on weighted voting but models based on discriminant function or classifier using feature values and class numbers could outperform the Oracle as well. Delivered conclusions are confirmed by the results of computer experiments carried out on benchmark and computer generated data.

  8. Feature Selection and Effective Classifiers.

    Science.gov (United States)

    Deogun, Jitender S.; Choubey, Suresh K.; Raghavan, Vijay V.; Sever, Hayri

    1998-01-01

    Develops and analyzes four algorithms for feature selection in the context of rough set methodology. Experimental results confirm the expected relationship between the time complexity of these algorithms and the classification accuracy of the resulting upper classifiers. When compared, results of upper classifiers perform better than lower…

  9. Evolving Classifiers: Methods for Incremental Learning

    CERN Document Server

    Hulley, Greg

    2007-01-01

    The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has shown good incremental learning capabilities on benchmark datasets on which the new ILUGA method has been tested. ILUGA has also shown good incremental learning ability using only a few classifiers and does not suffer from catastrophic forgetting. The results obtained for ILUGA on the Optical Character Recognition (OCR) and Wine datasets are good, with an overall accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT for the difficult multi-class OCR dataset.

  10. Hybrid k -Nearest Neighbor Classifier.

    Science.gov (United States)

    Yu, Zhiwen; Chen, Hantao; Liuxs, Jiming; You, Jane; Leung, Hareton; Han, Guoqiang

    2016-06-01

    Conventional k -nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches. Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space. Finally, the nonparametric tests are proposed to be adopted to compare the proposed method with other classification approaches over multiple datasets. The experiments on the real-world datasets from the Knowledge Extraction based on Evolutionary Learning dataset repository demonstrate that RS-HBKNN works well on real datasets, and outperforms most of the state-of-the-art classification approaches. PMID:26126291

  11. Classified

    CERN Multimedia

    Computer Security Team

    2011-01-01

    In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN.   But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...

  12. Evidential multinomial logistic regression for multiclass classifier calibration

    OpenAIRE

    Xu, Philippe; Davoine, Franck; Denoeux, Thierry

    2015-01-01

    The calibration of classifiers is an important task in information fusion. To compare or combine the outputs of several classifiers, they need to be represented in a common space. Probabilistic calibration methods transform the output of a classifier into a posterior probability distribution. In this paper, we introduce an evidential calibration method for multiclass classification problems. Our approach uses an extension of multinomial logistic regression to the theory of belief functions. W...

  13. Near-Optimal Evasion of Convex-Inducing Classifiers

    CERN Document Server

    Nelson, Blaine; Huang, Ling; Joseph, Anthony D; Lau, Shing-hon; Lee, Steven J; Rao, Satish; Tran, Anthony; Tygar, J D

    2010-01-01

    Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.

  14. Adaptively robust filtering with classified adaptive factors

    Institute of Scientific and Technical Information of China (English)

    CUI Xianqiang; YANG Yuanxi

    2006-01-01

    The key problems in applying the adaptively robust filtering to navigation are to establish an equivalent weight matrix for the measurements and a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information to the state parameter estimates. In this paper, an adaptively robust filtering with classified adaptive factors was proposed, based on the principles of the adaptively robust filtering and bi-factor robust estimation for correlated observations. According to the constant velocity model of Kalman filtering, the state parameter vector was divided into two groups, namely position and velocity. The estimator of the adaptively robust filtering with classified adaptive factors was derived, and the calculation expressions of the classified adaptive factors were presented. Test results show that the adaptively robust filtering with classified adaptive factors is not only robust in controlling the measurement outliers and the kinematic state disturbing but also reasonable in balancing the contributions of the predicted position and velocity, respectively, and its filtering accuracy is superior to the adaptively robust filter with single adaptive factor based on the discrepancy of the predicted position or the predicted velocity.

  15. Al-Hadith Text Classifier

    OpenAIRE

    Mohammed Naji Al-Kabi; Ghassan Kanaan; Riyad Al-Shalabi; Saja I. Al- Sinjilawi; Ronza S. Al- Mustafa

    2005-01-01

    This study explore the implementation of a text classification method to classify the prophet Mohammed (PBUH) hadiths (sayings) using Sahih Al-Bukhari classification. The sayings explain the Holy Qur`an, which considered by Muslims to be the direct word of Allah. Present method adopts TF/IDF (Term Frequency-Inverse Document Frequency) which is used usually for text search. TF/IDF was used for term weighting, in which document weights for the selected terms are computed, to classify non-vocali...

  16. 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....

  17. Semantic Features for Classifying Referring Search Terms

    Energy Technology Data Exchange (ETDEWEB)

    May, Chandler J.; Henry, Michael J.; McGrath, Liam R.; Bell, Eric B.; Marshall, Eric J.; Gregory, Michelle L.

    2012-05-11

    When an internet user clicks on a result in a search engine, a request is submitted to the destination web server that includes a referrer field containing the search terms given by the user. Using this information, website owners can analyze the search terms leading to their websites to better understand their visitors needs. This work explores some of the features that can be used for classification-based analysis of such referring search terms. We present initial results for the example task of classifying HTTP requests countries of origin. A system that can accurately predict the country of origin from query text may be a valuable complement to IP lookup methods which are susceptible to the obfuscation of dereferrers or proxies. We suggest that the addition of semantic features improves classifier performance in this example application. We begin by looking at related work and presenting our approach. After describing initial experiments and results, we discuss paths forward for this work.

  18. 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

  19. 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

  20. An ensemble of SVM classifiers based on gene pairs.

    Science.gov (United States)

    Tong, Muchenxuan; Liu, Kun-Hong; Xu, Chungui; Ju, Wenbin

    2013-07-01

    In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. PMID:23668348

  1. 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...

  2. Al-Hadith Text Classifier

    Directory of Open Access Journals (Sweden)

    Mohammed Naji Al-Kabi

    2005-01-01

    Full Text Available This study explore the implementation of a text classification method to classify the prophet Mohammed (PBUH hadiths (sayings using Sahih Al-Bukhari classification. The sayings explain the Holy Qur`an, which considered by Muslims to be the direct word of Allah. Present method adopts TF/IDF (Term Frequency-Inverse Document Frequency which is used usually for text search. TF/IDF was used for term weighting, in which document weights for the selected terms are computed, to classify non-vocalized sayings, after their terms (keywords have been transformed to the corresponding canonical form (i.e., roots, to one of eight Books (classes, according to Al-Bukhari classification. A term would have a higher weight if it were a good descriptor for a particular book, i.e., it appears frequently in the book but is infrequent in the entire corpus.

  3. Classifying self-gravitating radiations

    CERN Document Server

    Kim, Hyeong-Chan

    2016-01-01

    We study static systems of self-gravitating radiations confined in a sphere by using numerical and analytic calculations. We classify and analyze the solutions systematically. Due to the scaling symmetry, any solution can be represented as a segment of a solution curve on a plane of two-dimensional scale invariant variables. We find that a system can be conveniently parametrized by three parameters representing the solution curve, the scaling, and the system size, instead of the parameters defined at the outer boundary. The solution curves are classified to three types representing regular solutions, conically singular solutions with, and without an object which resembles an event horizon up to causal disconnectedness. For the last type, the behavior of a self-gravitating system is simple enough to allow analytic calculations.

  4. Disassembly and Sanitization of Classified Matter

    International Nuclear Information System (INIS)

    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

  5. Clustering signatures classify directed networks

    Science.gov (United States)

    Ahnert, S. E.; Fink, T. M. A.

    2008-09-01

    We use a clustering signature, based on a recently introduced generalization of the clustering coefficient to directed networks, to analyze 16 directed real-world networks of five different types: social networks, genetic transcription networks, word adjacency networks, food webs, and electric circuits. We show that these five classes of networks are cleanly separated in the space of clustering signatures due to the statistical properties of their local neighborhoods, demonstrating the usefulness of clustering signatures as a classifier of directed networks.

  6. Classifying Southern Hemisphere extratropical cyclones

    Science.gov (United States)

    Catto, Jennifer

    2015-04-01

    There is a wide variety of flavours of extratropical cyclones in the Southern Hemisphere, with differing structures and lifecycles. Previous studies have classified these manually using upper level flow features or satellite data. In order to be able to evaluate climate models and understand how extratropical cyclones might change in the future, we need to be able to use an automated method to classify cyclones. Extratropical cyclones have been identified in the Southern Hemisphere from the ERA-Interim reanalysis dataset with a commonly used identification and tracking algorithm that employs 850hPa relative vorticity. A clustering method applied to large-scale fields from ERA-Interim at the time of cyclone genesis (when the cyclone is first identified), has been used to objectively classify these cyclones in the Southern Hemisphere. This simple method is able to separate the cyclones into classes with quite different development mechanisms and lifecycle characteristics. Some of the classes seem to coincide with previous manual classifications on shorter timescales, showing their utility for climate model evaluation and climate change studies.

  7. Query Strategies for Evading Convex-Inducing Classifiers

    CERN Document Server

    Nelson, Blaine; Huang, Ling; Joseph, Anthony D; Lee, Steven J; Rao, Satish; Tygar, J D

    2010-01-01

    Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade detection while incurring a near-minimal cost of modifying their intended malfeasance. We generalize the theory of Lowd and Meek (2005) to the family of convex-inducing classifiers that partition input space into two sets one of which is convex. We present query algorithms for this family that construct undetected instances of approximately minimal cost using only polynomially-many queries in the dimension of the space and in the level of approximation. Our results demonstrate that near-optimal evasion can be accomplished without reverse-engineering the classifier's decision boundary. We also consider general lp costs and show that near-optimal evasion on the family of convex-inducing classifiers is generally efficient for both positive and negative convexity for all levels of approximation if p=1.

  8. Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers

    Directory of Open Access Journals (Sweden)

    Nowakowski Artur

    2015-10-01

    Full Text Available Boosting is a classification method which has been proven useful in non-satellite image processing while it is still new to satellite remote sensing. It is a meta-algorithm, which builds a strong classifier from many weak ones in iterative way. We adapt the AdaBoost.M1 boosting algorithm in a new land cover classification scenario based on utilization of very simple threshold classifiers employing spectral and contextual information. Thresholds for the classifiers are automatically calculated adaptively to data statistics.

  9. Waste classifying and separation device

    International Nuclear Information System (INIS)

    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.)

  10. 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...... cluster actors according to a number of key background characteristics and second assess how the categories of the different interest group typologies relate to these clusters. We demonstrate that background characteristics do align to a certain extent with certain interest group types but also find...

  11. Glycosylation site prediction using ensembles of Support Vector Machine classifiers

    Directory of Open Access Journals (Sweden)

    Silvescu Adrian

    2007-11-01

    Full Text Available Abstract Background Glycosylation is one of the most complex post-translational modifications (PTMs of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. Results We explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of Support Vector Machine classifiers and ensembles of Support Vector Machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of Support Vector Machine classifiers outperform single Support Vector Machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction. Conclusion Ensembles of Support Vector Machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.

  12. 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.

  13. Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers

    Science.gov (United States)

    Nowakowski, Artur

    2015-10-01

    Boosting is a classification method which has been proven useful in non-satellite image processing while it is still new to satellite remote sensing. It is a meta-algorithm, which builds a strong classifier from many weak ones in iterative way. We adapt the AdaBoost.M1 boosting algorithm in a new land cover classification scenario based on utilization of very simple threshold classifiers employing spectral and contextual information. Thresholds for the classifiers are automatically calculated adaptively to data statistics. The proposed method is employed for the exemplary problem of artificial area identification. Classification of IKONOS multispectral data results in short computational time and overall accuracy of 94.4% comparing to 94.0% obtained by using AdaBoost.M1 with trees and 93.8% achieved using Random Forest. The influence of a manipulation of the final threshold of the strong classifier on classification results is reported.

  14. A semi-automated approach to building text summarisation classifiers

    Directory of Open Access Journals (Sweden)

    Matias Garcia-Constantino

    2012-12-01

    Full Text Available An investigation into the extraction of useful information from the free text element of questionnaires, using a semi-automated summarisation extraction technique, is described. The summarisation technique utilises the concept of classification but with the support of domain/human experts during classifier construction. A realisation of the proposed technique, SARSET (Semi-Automated Rule Summarisation Extraction Tool, is presented and evaluated using real questionnaire data. The results of this evaluation are compared against the results obtained using two alternative techniques to build text summarisation classifiers. The first of these uses standard rule-based classifier generators, and the second is founded on the concept of building classifiers using secondary data. The results demonstrate that the proposed semi-automated approach outperforms the other two approaches considered.

  15. Combining Heterogeneous Classifiers for Relational Databases

    CERN Document Server

    Manjunatha, Geetha; Sitaram, Dinkar

    2012-01-01

    Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a 'flat' form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets, namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy.

  16. Classifying melodies using tree grammars

    OpenAIRE

    Bernabeu Briones, José Francisco; CALERA RUBIO, JORGE; Iñesta Quereda, José Manuel

    2011-01-01

    Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic...

  17. Entropic One-Class Classifiers.

    Science.gov (United States)

    Livi, Lorenzo; Sadeghian, Alireza; Pedrycz, Witold

    2015-12-01

    The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation-based approach; we embed the input data into the dissimilarity space (DS) by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented by weighted Euclidean graphs, which we use to determine the entropy of the data distribution in the DS and at the same time to derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking data sets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique. PMID:25879977

  18. On classifying digital accounting documents

    OpenAIRE

    Chih-Fong, Tsai

    2007-01-01

    Advances in computing and multimedia technologies allow many accounting documents to be digitized within little cost for effective storage and access. Moreover, the amount of accounting documents is increasing rapidly, this leads to the need of developing some mechanisms to effectively manage those (semi-structured) digital accounting documents for future accounting information systems (AIS). In general, accounting documents contains such as invoices, purchase orders, checks, photographs, cha...

  19. Cellular computation using classifier systems

    OpenAIRE

    Kelly, Ciaran; Decraene, James, Lobo, Victor; Mitchell, George G.; McMullin, Barry; O'Brien, Darragh

    2006-01-01

    The EU FP6 Integrated Project PACE ('Programmable Artificial Cell Evolution') is investigating the creation, de novo, of chemical 'protocells'. These will be minimal 'wetware' chemical systems integrating molecular information carriers, primitive energy conversion (metabolism) and containment (membrane). Ultimately they should be capable of autonomous reproduction, and be 'programmable' to realise specific desired function. A key objective of PACE is to explore the application of such pro...

  20. 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.

  1. A Neural Network Classifier of Volume Datasets

    CERN Document Server

    Zukić, Dženan; Kolb, Andreas

    2009-01-01

    Many state-of-the art visualization techniques must be tailored to the specific type of dataset, its modality (CT, MRI, etc.), the recorded object or anatomical region (head, spine, abdomen, etc.) and other parameters related to the data acquisition process. While parts of the information (imaging modality and acquisition sequence) may be obtained from the meta-data stored with the volume scan, there is important information which is not stored explicitly (anatomical region, tracing compound). Also, meta-data might be incomplete, inappropriate or simply missing. This paper presents a novel and simple method of determining the type of dataset from previously defined categories. 2D histograms based on intensity and gradient magnitude of datasets are used as input to a neural network, which classifies it into one of several categories it was trained with. The proposed method is an important building block for visualization systems to be used autonomously by non-experts. The method has been tested on 80 datasets,...

  2. Discrimination-Aware Classifiers for Student Performance Prediction

    Science.gov (United States)

    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…

  3. Rotary fluidized dryer classifier for coal

    Energy Technology Data Exchange (ETDEWEB)

    Sakaba, M.; Ueki, S.; Matsumoto, T.

    1985-01-01

    The development of equipment is reproted which uses a heat transfer medium and hot air to dry metallurgical coal to a predetermined moisture level, and which simultaneously classifies the dust-producing fine coal content. The integral construction of the drying and classifying zones results in a very compact configuration, with an installation area of 1/2 to 1/3 of that required for systems in which a separate dryer and classifier are combined. 6 references.

  4. Examining the significance of fingerprint-based classifiers

    Directory of Open Access Journals (Sweden)

    Collins Jack R

    2008-12-01

    Full Text Available Abstract Background Experimental examinations of biofluids to measure concentrations of proteins or their fragments or metabolites are being explored as a means of early disease detection, distinguishing diseases with similar symptoms, and drug treatment efficacy. Many studies have produced classifiers with a high sensitivity and specificity, and it has been argued that accurate results necessarily imply some underlying biology-based features in the classifier. The simplest test of this conjecture is to examine datasets designed to contain no information with classifiers used in many published studies. Results The classification accuracy of two fingerprint-based classifiers, a decision tree (DT algorithm and a medoid classification algorithm (MCA, are examined. These methods are used to examine 30 artificial datasets that contain random concentration levels for 300 biomolecules. Each dataset contains between 30 and 300 Cases and Controls, and since the 300 observed concentrations are randomly generated, these datasets are constructed to contain no biological information. A modest search of decision trees containing at most seven decision nodes finds a large number of unique decision trees with an average sensitivity and specificity above 85% for datasets containing 60 Cases and 60 Controls or less, and for datasets with 90 Cases and 90 Controls many DTs have an average sensitivity and specificity above 80%. For even the largest dataset (300 Cases and 300 Controls the MCA procedure finds several unique classifiers that have an average sensitivity and specificity above 88% using only six or seven features. Conclusion While it has been argued that accurate classification results must imply some biological basis for the separation of Cases from Controls, our results show that this is not necessarily true. The DT and MCA classifiers are sufficiently flexible and can produce good results from datasets that are specifically constructed to contain no

  5. A Vertical Search Engine – Based On Domain Classifier

    OpenAIRE

    Rajashree Shettar; Rahul Bhuptani

    2008-01-01

    The World Wide Web is growing exponentially and the dynamic, unstructured nature of the web makes it difficult to locate useful resources. Web Search engines such as Google and Alta Vista provide huge amount of information many of which might not be relevant to the users query. In this paper, we build a vertical search engine which takes a seed URL and classifies the URLs crawled as Medical or Finance domains. The filter component of the vertical search engine classifies the web pages downloa...

  6. Serefind: A Social Networking Website for Classifieds

    OpenAIRE

    Verma, Pramod

    2014-01-01

    This paper presents the design and implementation of a social networking website for classifieds, called Serefind. We designed search interfaces with focus on security, privacy, usability, design, ranking, and communications. We deployed this site at the Johns Hopkins University, and the results show it can be used as a self-sustaining classifieds site for public or private communities.

  7. A review of learning vector quantization classifiers

    CERN Document Server

    Nova, David

    2015-01-01

    In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.

  8. A fuzzy classifier system for process control

    Science.gov (United States)

    Karr, C. L.; Phillips, J. C.

    1994-01-01

    A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

  9. 32 CFR 775.5 - Classified actions.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 5 2010-07-01 2010-07-01 false Classified actions. 775.5 Section 775.5 National Defense Department of Defense (Continued) DEPARTMENT OF THE NAVY MISCELLANEOUS RULES PROCEDURES FOR IMPLEMENTING THE NATIONAL ENVIRONMENTAL POLICY ACT § 775.5 Classified actions. (a) The fact that a...

  10. Classifying climate change adaptation frameworks

    Science.gov (United States)

    Armstrong, Jennifer

    2014-05-01

    Complex socio-ecological demographics are factors that must be considered when addressing adaptation to the potential effects of climate change. As such, a suite of deployable climate change adaptation frameworks is necessary. Multiple frameworks that are required to communicate the risks of climate change and facilitate adaptation. Three principal adaptation frameworks have emerged from the literature; Scenario - Led (SL), Vulnerability - Led (VL) and Decision - Centric (DC). This study aims to identify to what extent these adaptation frameworks; either, planned or deployed are used in a neighbourhood vulnerable to climate change. This work presents a criterion that may be used as a tool for identifying the hallmarks of adaptation frameworks and thus enabling categorisation of projects. The study focussed on the coastal zone surrounding the Sizewell nuclear power plant in Suffolk in the UK. An online survey was conducted identifying climate change adaptation projects operating in the study area. This inventory was analysed to identify the hallmarks of each adaptation project; Levels of dependency on climate model information, Metrics/units of analysis utilised, Level of demographic knowledge, Level of stakeholder engagement, Adaptation implementation strategies and Scale of adaptation implementation. The study found that climate change adaptation projects could be categorised, based on the hallmarks identified, in accordance with the published literature. As such, the criterion may be used to establish the matrix of adaptation frameworks present in a given area. A comprehensive summary of the nature of adaptation frameworks in operation in a locality provides a platform for further comparative analysis. Such analysis, enabled by the criterion, may aid the selection of appropriate frameworks enhancing the efficacy of climate change adaptation.

  11. 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.

  12. Designing Kernel Scheme for Classifiers Fusion

    CERN Document Server

    Haghighi, Mehdi Salkhordeh; Vahedian, Abedin; Modaghegh, Hamed

    2009-01-01

    In this paper, we propose a special fusion method for combining ensembles of base classifiers utilizing new neural networks in order to improve overall efficiency of classification. While ensembles are designed such that each classifier is trained independently while the decision fusion is performed as a final procedure, in this method, we would be interested in making the fusion process more adaptive and efficient. This new combiner, called Neural Network Kernel Least Mean Square1, attempts to fuse outputs of the ensembles of classifiers. The proposed Neural Network has some special properties such as Kernel abilities,Least Mean Square features, easy learning over variants of patterns and traditional neuron capabilities. Neural Network Kernel Least Mean Square is a special neuron which is trained with Kernel Least Mean Square properties. This new neuron is used as a classifiers combiner to fuse outputs of base neural network classifiers. Performance of this method is analyzed and compared with other fusion m...

  13. Classifying Unidentified Gamma-ray Sources

    CERN Document Server

    Salvetti, David

    2016-01-01

    During its first 2 years of mission the Fermi-LAT instrument discovered more than 1,800 gamma-ray sources in the 100 MeV to 100 GeV range. Despite the application of advanced techniques to identify and associate the Fermi-LAT sources with counterparts at other wavelengths, about 40% of the LAT sources have no a clear identification remaining "unassociated". The purpose of my Ph.D. work has been to pursue a statistical approach to identify the nature of each Fermi-LAT unassociated source. To this aim, we implemented advanced machine learning techniques, such as logistic regression and artificial neural networks, to classify these sources on the basis of all the available gamma-ray information about location, energy spectrum and time variability. These analyses have been used for selecting targets for AGN and pulsar searches and planning multi-wavelength follow-up observations. In particular, we have focused our attention on the search of possible radio-quiet millisecond pulsar (MSP) candidates in the sample of...

  14. 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.

  15. Parallelism and programming in classifier systems

    CERN Document Server

    Forrest, Stephanie

    1990-01-01

    Parallelism and Programming in Classifier Systems deals with the computational properties of the underlying parallel machine, including computational completeness, programming and representation techniques, and efficiency of algorithms. In particular, efficient classifier system implementations of symbolic data structures and reasoning procedures are presented and analyzed in detail. The book shows how classifier systems can be used to implement a set of useful operations for the classification of knowledge in semantic networks. A subset of the KL-ONE language was chosen to demonstrate these o

  16. Classifier Risk Estimation under Limited Labeling Resources

    OpenAIRE

    Kumar, Anurag; Raj, Bhiksha

    2016-01-01

    In this paper we propose strategies for estimating performance of a classifier when labels cannot be obtained for the whole test set. The number of test instances which can be labeled is very small compared to the whole test data size. The goal then is to obtain a precise estimate of classifier performance using as little labeling resource as possible. Specifically, we try to answer, how to select a subset of the large test set for labeling such that the performance of a classifier estimated ...

  17. Dengue—How Best to Classify It

    OpenAIRE

    Srikiatkhachorn, Anon; Rothman, Alan L.; Robert V Gibbons; Sittisombut, Nopporn; Malasit, Prida; Ennis, Francis A.; Nimmannitya, Suchitra; Kalayanarooj, Siripen

    2011-01-01

    Since the 1970s, dengue has been classified as dengue fever and dengue hemorrhagic fever. In 2009, the World Health Organization issued a new, severity-based clinical classification which differs greatly from the previous classification.

  18. 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).

  19. Classifying Genomic Sequences by Sequence Feature Analysis

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Liu; Dian Jiao; Xiao Sun

    2005-01-01

    Traditional sequence analysis depends on sequence alignment. In this study, we analyzed various functional regions of the human genome based on sequence features, including word frequency, dinucleotide relative abundance, and base-base correlation. We analyzed the human chromosome 22 and classified the upstream,exon, intron, downstream, and intergenic regions by principal component analysis and discriminant analysis of these features. The results show that we could classify the functional regions of genome based on sequence feature and discriminant analysis.

  20. Nomograms for Visualization of Naive Bayesian Classifier

    OpenAIRE

    Možina, Martin; Demšar, Janez; Michael W Kattan; Zupan, Blaz

    2004-01-01

    Besides good predictive performance, the naive Bayesian classifier can also offer a valuable insight into the structure of the training data and effects of the attributes on the class probabilities. This structure may be effectively revealed through visualization of the classifier. We propose a new way to visualize the naive Bayesian model in the form of a nomogram. The advantages of the proposed method are simplicity of presentation, clear display of the effects of individual attribute value...

  1. Probabilistic classifiers with high-dimensional data

    OpenAIRE

    Kim, Kyung In; Simon, Richard

    2010-01-01

    For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probab...

  2. Classifier Aggregation Using Local Classification Confidence

    Czech Academy of Sciences Publication Activity Database

    Štefka, David; Holeňa, Martin

    Setúbal: INSTICC, 2009, s. 173-178. ISBN 978-989-8111-66-1. [ICAART 2009. International Conference on Agents and Artificial Intelligence /1./. Porto (PT), 19.01.2009-21.01.2009] R&D Projects: GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : classifier aggregation * classifier combining * classification confidence Subject RIV: IN - Informatics, Computer Science

  3. Binary Classifier Calibration: Non-parametric approach

    OpenAIRE

    Naeini, Mahdi Pakdaman; Cooper, Gregory F.; Hauskrecht, Milos

    2014-01-01

    Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decision-making tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are well-calibrated, ab initio. The other approach is to use some post-processing methods for transforming the output of a classifier to be well calibrated, as for example histogram binning, Platt scaling, and is...

  4. COMBINING CLASSIFIERS FOR CREDIT RISK PREDICTION

    Institute of Scientific and Technical Information of China (English)

    Bhekisipho TWALA

    2009-01-01

    Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.

  5. Management Education: Classifying Business Curricula and Conceptualizing Transfers and Bridges

    OpenAIRE

    Davar Rezania; Mike Henry

    2010-01-01

    Traditionally, higher academic education has favoured acquisition of individualized conceptual knowledge over context-independent procedural knowledge. Applied degrees, on the other hand, favour procedural knowledge. We present a conceptual model for classifying a business curriculum. This classification can inform discussion around difficulties associated with issues such as assessment of prior learning, as well as transfers and bridges from applied degrees to baccalaureate degrees in busine...

  6. Mathematical Modeling and Analysis of Classified Marketing of Agricultural Products

    Institute of Scientific and Technical Information of China (English)

    Fengying; WANG

    2014-01-01

    Classified marketing of agricultural products was analyzed using the Logistic Regression Model. This method can take full advantage of information in agricultural product database,to find factors influencing best selling degree of agricultural products,and make quantitative analysis accordingly. Using this model,it is also able to predict sales of agricultural products,and provide reference for mapping out individualized sales strategy for popularizing agricultural products.

  7. LEAF FEATURES EXTRACTION AND RECOGNITION APPROACHES TO CLASSIFY PLANT

    OpenAIRE

    Mohamad Faizal Ab Jabal; Suhardi Hamid; Salehuddin Shuib; Illiasaak Ahmad

    2013-01-01

    Plant classification based on leaf identification is becoming a popular trend. Each leaf carries substantial information that can be used to identify and classify the origin or the type of plant. In medical perspective, images have been used by doctors to diagnose diseases and this method has been proven reliable for years. Using the same method as doctors, researchers try to simulate the same principle to recognise a plant using high quality leaf images and complex mathematical formulae for ...

  8. Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

    OpenAIRE

    Sang-Hoon Hong; Hyun-Ok Kim; Shimon Wdowinski; Emanuelle Feliciano

    2015-01-01

    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 ...

  9. Weighted Hybrid Decision Tree Model for Random Forest Classifier

    Science.gov (United States)

    Kulkarni, Vrushali Y.; Sinha, Pradeep K.; Petare, Manisha C.

    2016-06-01

    Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.

  10. Packet Payload Inspection Classifier in the Network Flow Level

    Directory of Open Access Journals (Sweden)

    N.Kannaiya Raja

    2012-06-01

    Full Text Available The network have in the world highly congested channels and topology which was dynamically created with high risk. In this we need flow classifier to find the packet movement in the network. In this paper we have to be developed and evaluated TCP/UDP/FTP/ICMP based on payload information and port numbers and number of flags in the packet for highly flow of packets in the network. The primary motivations of this paper all the valuable protocols are used legally to process find out the end user by using payload packet inspection, and also used evaluations hypothesis testing approach. The effective use of tamper resistant flow classifier has used in one network contexts domain and developed in a different Berkeley and Cambridge, the classification and accuracy was easily found through the packet inspection by using different flags in the packets. While supervised classifier training specific to the new domain results in much better classification accuracy, we also formed a new approach to determine malicious packet and find a packet flow classifier and send correct packet to destination address.

  11. An ensemble self-training protein interaction article classifier.

    Science.gov (United States)

    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. PMID:24212028

  12. Diagnostic value of perfusion MRI in classifying stroke

    International Nuclear Information System (INIS)

    Our study was designed to determine whether supplementary information obtained with perfusion MRI can enhance accuracy. We used delayed perfusion, as represented by time to peak map on perfusion MRI, to classify strokes in 39 patients. Strokes were classified as hemodynamic if delayed perfusion extended to a whole territory of the occluded arterial trunk; as embolic if delayed perfusion was absent or restricted to infarcts; as arteriosclerotic if infarcts were small, multiple, and located mainly in the basal ganglias; or as unclassified if the pathophysiology was unclear. We compared these findings with vascular lesions on cerebral angiography, neurological signs, infarction on MRI, ischemia on xenon-enhanced CT (Xe/CT) and collateral pathway development. Delayed perfusion clearly indicated the area of arterial occlusion. Strokes were classified as hemodynamic in 13 patients, embolic in 14 patients, arteriosclerotic in 6 patients and unclassified in 6 patients. Hemodynamic infarcts were seen only in deep white-matter areas such as the centrum semiovale or corona radiata, whereas embolic infarcts were in the cortex, cortex and subjacent white matter, and lenticulo-striatum. Embolic and arteriosclerotic infarcts occurred even in hemo-dynamically compromized hemispheres. Our findings indicate that perfusion MRI, in association with adetailed analysis of T2-weighted MRI of cerebral infarcts in the axial and coronal planes, can accurately classify stroke as hemodynamic, embolic or arteriosclerotic. (author)

  13. A Vertical Search Engine – Based On Domain Classifier

    Directory of Open Access Journals (Sweden)

    Rajashree Shettar

    2008-11-01

    Full Text Available The World Wide Web is growing exponentially and the dynamic, unstructured nature of the web makes it difficult to locate useful resources. Web Search engines such as Google and Alta Vista provide huge amount of information many of which might not be relevant to the users query. In this paper, we build a vertical search engine which takes a seed URL and classifies the URLs crawled as Medical or Finance domains. The filter component of the vertical search engine classifies the web pages downloaded by the crawler into appropriate domains. The web pages crawled is checked for relevance based on the domain chosen and indexed. External users query the database with keywords to search; The Domain classifiers classify the URLs into relevant domain and are presented in descending order according to the rank number. This paper focuses on two issues – page relevance to a particular domain and page contents for the search keywords to improve the quality of URLs to be listed thereby avoiding irrelevant or low-quality ones .

  14. Dynamic Bayesian Combination of Multiple Imperfect Classifiers

    CERN Document Server

    Simpson, Edwin; Psorakis, Ioannis; Smith, Arfon

    2012-01-01

    Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed. Finally we present ...

  15. Adapt Bagging to Nearest Neighbor Classifiers

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou; Yang Yu

    2005-01-01

    It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.

  16. 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...

  17. Enhancing atlas based segmentation with multiclass linear classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr [Université de Lyon, CREATIS, CNRS UMR 5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne 69300 (France)

    2015-12-15

    Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.

  18. Enhancing atlas based segmentation with multiclass linear classifiers

    International Nuclear Information System (INIS)

    Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy

  19. Peat classified as slowly renewable biomass fuel

    International Nuclear Information System (INIS)

    The expert group, appointed by the Finnish Ministry of Trade and Industry, consisting of Dr. Patrick Crill from USA, Dr. Ken Hargreaves from UK and college lecturer Atte Korhola from Finland, studied the role of peat in Finnish greenhouse gas emissions. The group did not produce new research information, the report of the group based on the present research data available in Finland on greenhouse gas balances of Finnish mires and peat utilization, how much greenhouse gases, e.g. methane, CO2 and N2O are liberated and bound by the mires. All the virgin peatlands in Finland (4.0 million ha), forest drained peatlands (5.7 million ha), peatlands used as fields in agriculture (0.25 million ha), peat harvesting and storage, as well as the actual peat production areas (0.063 million ha) are reviewed. The main factor intensifying the greenhouse effect, so called radiate forcing, is estimated to be the methane emissions from virgin peatlands, 11 million CO2 equivalent tons per year. The next largest sources of emissions are estimated to be the CO2 emissions of peat (8 million t/a), CO2 emissions from peatlands in agricultural use (3.2 - 7.8 million t/a), the N2O emissions (over 2 million t/a) and methane emissions (less than 2 million t/a) of forest ditched peatlands. Other emission sources such as actual peat production and transportation are minimal. Largest carbon sinks are clearly forest-drained peatlands (9.4 - 14.9 million t/a) and virgin peatlands (more than 3 million t/a). Main conclusions of the experts group is that peat is formed continuously via photosynthesis of mosses, sedges and under-shrub vegetation and via forest litter formation. The report discovers that the basics of the formation of peat biomass is similar to that of other plant-based biomasses, such as wood, but the time required by stratification is different. Forests in Southern Finland become ready for harvesting in about 100 years, but the formation of commercially viable peat layers takes

  20. 28 CFR 17.41 - Access to classified information.

    Science.gov (United States)

    2010-07-01

    ... personal and professional history affirmatively indicated loyalty to the United States, strength of... does not discriminate on the basis of race, color, religion, sex, national origin, disability,...

  1. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    International Nuclear Information System (INIS)

    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.

  2. CLASSIFYING NODULAR LESIONS OF ORAL CAVITY

    OpenAIRE

    Sumit Bhateja

    2013-01-01

    Diagnosis of many lesions of the oral cavity is challenging to most cliniciansbecause of their uncommon prevalence. A number of cystic, osteodystrophic,microbial, tumor and tumor like lesions of the oral cavity are present withcharacteristic exophytic/raised surface; which makes their diagnosis and studysimpler. The present article is attempted at classifying the common nodular lesions ofthe oral cavity.

  3. The classifying topos of a topological bicategory

    CERN Document Server

    Bakovic, Igor

    2009-01-01

    For any topological bicategory 2C, the Duskin nerve N2C of 2C is a simplicial space. We introduce the classifying topos B2C of 2C as the Deligne topos of sheaves Sh(N2C) on the simplicial space 2NC. It is shown that the category of topos morphisms from the topos of sheaves Sh(X) on a topological space X to the Deligne classifying topos Sh(N2C) is naturally equivalent to the category of principal 2C-bundles. As a simple consequence, the geometric realization of the nerve N2C of a locally contractible topological bicategory 2C is the classifying space of principal 2C-bundles (on CW complexes), giving a variant of the result of Baas, Bokstedt and Kro derived in the context of bicategorical K-theory. We also define classifying topoi of a topological bicategory 2C using sheaves on other types of nerves of a bicategory given by Lack and Paoli, Simpson and Tamsamani by means of bisimplicial spaces, and we examine their properties.

  4. Automated mobility-classified-aerosol detector

    OpenAIRE

    Russell, Lynn M.; Flagan, Richard C.; Zhang, Shou-Hua

    2001-01-01

    An aerosol detection system for measuring particle number distribution with respect to particle dimension in an aerosol sample. The system includes an alternating dual-bag sampler, a radially classified differential mobility analyzer, and a condensation nucleus counter. Pressure variations in sampling are compensated by feedback control of volumetric flow rates using a plurality of flow control elements.

  5. Embedded feature ranking for ensemble MLP classifiers

    OpenAIRE

    Windeatt, T; Duangsoithong, R; Smith, R

    2011-01-01

    A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

  6. Classification Studies in an Advanced Air Classifier

    Science.gov (United States)

    Routray, Sunita; Bhima Rao, R.

    2016-01-01

    In the present paper, experiments are carried out using VSK separator which is an advanced air classifier to recover heavy minerals from beach sand. In classification experiments the cage wheel speed and the feed rate are set and the material is fed to the air cyclone and split into fine and coarse particles which are collected in separate bags. The size distribution of each fraction was measured by sieve analysis. A model is developed to predict the performance of the air classifier. The objective of the present model is to predict the grade efficiency curve for a given set of operating parameters such as cage wheel speed and feed rate. The overall experimental data with all variables studied in this investigation is fitted to several models. It is found that the present model is fitting good to the logistic model.

  7. NETWORK FAULT DIAGNOSIS USING DATA MINING CLASSIFIERS

    Directory of Open Access Journals (Sweden)

    Eleni Rozaki

    2015-04-01

    Full Text Available Mobile networks are under more pressure than ever before because of the increasing number of smartphone users and the number of people relying on mobile data networks. With larger numbers of users, the issue of service quality has become more important for network operators. Identifying faults in mobile networks that reduce the quality of service must be found within minutes so that problems can be addressed and networks returned to optimised performance. In this paper, a method of automated fault diagnosis is presented using decision trees, rules and Bayesian classifiers for visualization of network faults. Using data mining techniques the model classifies optimisation criteria based on the key performance indicators metrics to identify network faults supporting the most efficient optimisation decisions. The goal is to help wireless providers to localize the key performance indicator alarms and determine which Quality of Service factors should be addressed first and at which locations.

  8. Design and evaluation of neural classifiers

    DEFF Research Database (Denmark)

    Hintz-Madsen, Mads; Pedersen, Morten With; Hansen, Lars Kai; Larsen, Jan

    In this paper we propose a method for the design of feedforward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme we derive a modified form of the entropy error measure and an algebraic estimate of the test error. In conjunction wit...... optimal brain damage pruning the test error estimate is used to optimize the network architecture. The scheme is evaluated on an artificial and a real world problem......In this paper we propose a method for the design of feedforward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme we derive a modified form of the entropy error measure and an algebraic estimate of the test error. In conjunction with...

  9. Robot Learning Using Learning Classifier Systems Approach

    OpenAIRE

    Jabin, Suraiya

    2010-01-01

    In this chapter, I have presented Learning Classifier Systems, which add to the classical Reinforcement Learning framework the possibility of representing the state as a vector of attributes and finding a compact expression of the representation so induced. Their formalism conveys a nice interaction between learning and evolution, which makes them a class of particularly rich systems, at the intersection of several research domains. As a result, they profit from the accumulated extensions of ...

  10. Classifiers Based on Inverted Distances. Chapter 19

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    Rijeka: InTech, 2011 - (Funatsu, K.; Hasegawa, K.), s. 369-386 ISBN 978-953-307-547-1 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : classification * neighbor distances * correlation dimension * Zipfian distribution Subject RIV: BB - Applied Statistics, Operational Research http://www.intechopen.com/books/new-fundamental-technologies-in-data-mining/classifiers-based-on-inverted-distances

  11. Using Singularity Exponent in Distance based Classifier

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    Los Alamitos : IEEE, 2010, s. 220-224 ISBN 978-1-4244-8135-4. [ISDA 2010. International Conference on Intelligent Systems Design and Applications /10./. Cairo (EG), 29.11.2010-01.12.2010] R&D Projects: GA MŠk 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : singularity exponent * nearest neighbor * classifier Subject RIV: IN - Informatics, Computer Science

  12. Classifying and evaluating architecture design methods

    OpenAIRE

    Aksit, Mehmet; Tekinerdogan, Bedir

    1999-01-01

    The concept of software architecture has gained a wide popularity and is generally considered to play a fundamental role in coping with the inherent difficulties of the development of large-scale and complex software systems. This document first gives a definition of architectures. Second, a meta-model for architecture design methods is presented. This model is used for classifying and evaluating various architecture design approaches. The document concludes with the description of the identi...

  13. 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 ostatní: 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

  14. A space-based radio frequency transient event classifier

    Energy Technology Data Exchange (ETDEWEB)

    Moore, K.R.; Blain, C.P.; Caffrey, M.P.; Franz, R.C.; Henneke, K.M.; Jones, R.G.

    1998-03-01

    The Department of Energy is currently investigating economical and reliable techniques for space-based nuclear weapon treaty verification. Nuclear weapon detonations produce RF transients that are signatures of illegal nuclear weapons tests. However, there are many other sources of RF signals, both natural and man-made. Direct digitization of RF signals requires rates of 300 MSamples per second and produces 10{sup 13} samples per day of data to analyze. it is impractical to store and downlink all digitized RF data from such a satellite without a prohibitively expensive increase in the number and capacities of ground stations. Reliable and robust data processing and information extraction must be performed onboard the spacecraft in order to reduce downlinked data to a reasonable volume. The FORTE (Fast On-Orbit Recording of Transient Events) satellite records RF transients in space. These transients will be classified onboard the spacecraft with an Event Classifier specialized hardware that performs signal preprocessing and neural network classification. The authors describe the Event Classifier requirements, scientific constraints, design and implementation.

  15. Automative Multi Classifier Framework for Medical Image Analysis

    Directory of Open Access Journals (Sweden)

    R. Edbert Rajan

    2015-04-01

    Full Text Available Medical image processing is the technique used to create images of the human body for medical purposes. Nowadays, medical image processing plays a major role and a challenging solution for the critical stage in the medical line. Several researches have done in this area to enhance the techniques for medical image processing. However, due to some demerits met by some advanced technologies, there are still many aspects that need further development. Existing study evaluate the efficacy of the medical image analysis with the level-set shape along with fractal texture and intensity features to discriminate PF (Posterior Fossa tumor from other tissues in the brain image. To develop the medical image analysis and disease diagnosis, to devise an automotive subjective optimality model for segmentation of images based on different sets of selected features from the unsupervised learning model of extracted features. After segmentation, classification of images is done. The classification is processed by adapting the multiple classifier frameworks in the previous work based on the mutual information coefficient of the selected features underwent for image segmentation procedures. In this study, to enhance the classification strategy, we plan to implement enhanced multi classifier framework for the analysis of medical images and disease diagnosis. The performance parameter used for the analysis of the proposed enhanced multi classifier framework for medical image analysis is Multiple Class intensity, image quality, time consumption.

  16. Image Classifying Registration for Gaussian & Bayesian Techniques: A Review

    Directory of Open Access Journals (Sweden)

    Rahul Godghate,

    2014-04-01

    Full Text Available A Bayesian Technique for Image Classifying Registration to perform simultaneously image registration and pixel classification. Medical image registration is critical for the fusion of complementary information about patient anatomy and physiology, for the longitudinal study of a human organ over time and the monitoring of disease development or treatment effect, for the statistical analysis of a population variation in comparison to a so-called digital atlas, for image-guided therapy, etc. A Bayesian Technique for Image Classifying Registration is well-suited to deal with image pairs that contain two classes of pixels with different inter-image intensity relationships. We will show through different experiments that the model can be applied in many different ways. For instance if the class map is known, then it can be used for template-based segmentation. If the full model is used, then it can be applied to lesion detection by image comparison. Experiments have been conducted on both real and simulated data. It show that in the presence of an extra-class, the classifying registration improves both the registration and the detection, especially when the deformations are small. The proposed model is defined using only two classes but it is straightforward to extend it to an arbitrary number of classes.

  17. Analysis of classifiers performance for classification of potential microcalcification

    Science.gov (United States)

    M. N., Arun K.; Sheshadri, H. S.

    2013-07-01

    Breast cancer is a significant public health problem in the world. According to the literature early detection improve breast cancer prognosis. Mammography is a screening tool used for early detection of breast cancer. About 10-30% cases are missed during the routine check as it is difficult for the radiologists to make accurate analysis due to large amount of data. The Microcalcifications (MCs) are considered to be important signs of breast cancer. It has been reported in literature that 30% - 50% of breast cancer detected radio graphically show MCs on mammograms. Histologic examinations report 62% to 79% of breast carcinomas reveals MCs. MC are tiny, vary in size, shape, and distribution, and MC may be closely connected to surrounding tissues. There is a major challenge using the traditional classifiers in the classification of individual potential MCs as the processing of mammograms in appropriate stage generates data sets with an unequal amount of information for both classes (i.e., MC, and Not-MC). Most of the existing state-of-the-art classification approaches are well developed by assuming the underlying training set is evenly distributed. However, they are faced with a severe bias problem when the training set is highly imbalanced in distribution. This paper addresses this issue by using classifiers which handle the imbalanced data sets. In this paper, we also compare the performance of classifiers which are used in the classification of potential MC.

  18. 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.

  19. Image Classifying Registration and Dynamic Region Merging

    Directory of Open Access Journals (Sweden)

    Himadri Nath Moulick

    2013-07-01

    Full Text Available In this paper, we address a complex image registration issue arising when the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequentlyencountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weights the two mixture components. The registration problem is formulated both as an energy minimization problem and as a Maximum A Posteriori (MAP estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated at the same time, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into

  20. Integrating language models into classifiers for BCI communication: a review

    Science.gov (United States)

    Speier, W.; Arnold, C.; Pouratian, N.

    2016-06-01

    Objective. The present review systematically examines the integration of language models to improve classifier performance in brain–computer interface (BCI) communication systems. Approach. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. Main results. Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. Significance. Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.

  1. On-line computing in a classified environment

    International Nuclear Information System (INIS)

    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

  2. 36 CFR 1260.22 - Who is responsible for the declassification of classified national security White House...

    Science.gov (United States)

    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,...

  3. Quality of life in urban-classified and rural-classified English local authority areas

    OpenAIRE

    Josep M. Campanera; Paul Higgins

    2011-01-01

    This paper presents the results of an analysis of the Audit Commission’s local quality-of-life indicators dataset to compare reported outcomes amongst 208 urban-classified and 144 rural-classified English local authority areas. We contextualise the demarcation of the urban and rural by reference to the transformational politics of the previous Labour government and its establishment of the sustainable communities initiative, whose controversial ‘place-based’ revitalisation essence continues t...

  4. A classifier neural network for rotordynamic systems

    Science.gov (United States)

    Ganesan, R.; Jionghua, Jin; Sankar, T. S.

    1995-07-01

    A feedforward backpropagation neural network is formed to identify the stability characteristic of a high speed rotordynamic system. The principal focus resides in accounting for the instability due to the bearing clearance effects. The abnormal operating condition of 'normal-loose' Coulomb rub, that arises in units supported by hydrodynamic bearings or rolling element bearings, is analysed in detail. The multiple-parameter stability problem is formulated and converted to a set of three-parameter algebraic inequality equations. These three parameters map the wider range of physical parameters of commonly-used rotordynamic systems into a narrow closed region, that is used in the supervised learning of the neural network. A binary-type state of the system is expressed through these inequalities that are deduced from the analytical simulation of the rotor system. Both the hidden layer as well as functional-link networks are formed and the superiority of the functional-link network is established. Considering the real time interpretation and control of the rotordynamic system, the network reliability and the learning time are used as the evaluation criteria to assess the superiority of the functional-link network. This functional-link network is further trained using the parameter values of selected rotor systems, and the classifier network is formed. The success rate of stability status identification is obtained to assess the potentials of this classifier network. The classifier network is shown that it can also be used, for control purposes, as an 'advisory' system that suggests the optimum way of parameter adjustment.

  5. Support Vector classifiers for Land Cover Classification

    CERN Document Server

    Pal, Mahesh

    2008-01-01

    Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and artificial neural network methods in terms of classification accuracy. Our results show that the SVM achieves a higher level of classification accuracy than either the maximum likelihood or the neural classifier, and that the support vector machine can be used with small training datasets and high-dimensional data.

  6. 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. 

  7. Learnability of min-max pattern classifiers

    Science.gov (United States)

    Yang, Ping-Fai; Maragos, Petros

    1991-11-01

    This paper introduces the class of thresholded min-max functions and studies their learning under the probably approximately correct (PAC) model introduced by Valiant. These functions can be used as pattern classifiers of both real-valued and binary-valued feature vectors. They are a lattice-theoretic generalization of Boolean functions and are also related to three-layer perceptrons and morphological signal operators. Several subclasses of the thresholded min- max functions are shown to be learnable under the PAC model.

  8. Classifying LEP Data with Support Vector Algorithms

    CERN Document Server

    Vannerem, P; Schölkopf, B; Smola, A J; Söldner-Rembold, S

    1999-01-01

    We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other classifiers such as Support Vector Machines has not yet been tested in this environment. We chose two different problems to compare the performance of a Support Vector Machine and a Neural Net trained with back-propagation: tagging events of the type e+e- -> ccbar and the identification of muons produced in multihadronic e+e- annihilation events.

  9. Comparison of Current Frame-Based Phoneme Classifiers

    Directory of Open Access Journals (Sweden)

    Vaclav Pfeifer

    2011-01-01

    Full Text Available This paper compares today’s most common frame-based classifiers. These classifiers can be divided into the two main groups – generic classifiers which creates the most probable model based on the training data (for example GMM and discriminative classifiers which focues on creating decision hyperplane. A lot of research has been done with the GMM classifiers and therefore this paper will be mainly focused on the frame-based classifiers. Two discriminative classifiers will be presented. These classifiers implements a hieararchical tree root structure over the input phoneme group which shown to be an effective. Based on these classifiers, two efficient training algorithms will be presented. We demonstrate advantages of our training algorithms by evaluating all classifiers over the TIMIT speech corpus.

  10. Cross-classified occupational exposure data.

    Science.gov (United States)

    Jones, Rachael M; Burstyn, Igor

    2016-09-01

    We demonstrate the regression analysis of exposure determinants using cross-classified random effects in the context of lead exposures resulting from blasting surfaces in advance of painting. We had three specific objectives for analysis of the lead data, and observed: (1) high within-worker variability in personal lead exposures, explaining 79% of variability; (2) that the lead concentration outside of half-mask respirators was 2.4-fold higher than inside supplied-air blasting helmets, suggesting that the exposure reduction by blasting helmets may be lower than expected by the Assigned Protection Factor; and (3) that lead concentrations at fixed area locations in containment were not associated with personal lead exposures. In addition, we found that, on average, lead exposures among workers performing blasting and other activities was 40% lower than among workers performing only blasting. In the process of obtaining these analyses objectives, we determined that the data were non-hierarchical: repeated exposure measurements were collected for a worker while the worker was a member of several groups, or cross-classified among groups. Since the worker is a member of multiple groups, the exposure data do not adhere to the traditionally assumed hierarchical structure. Forcing a hierarchical structure on these data led to similar within-group and between-group variability, but decreased precision in the estimate of effect of work activity on lead exposure. We hope hygienists and exposure assessors will consider non-hierarchical models in the design and analysis of exposure assessments. PMID:27029937

  11. 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.

  12. 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.

  13. Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

    OpenAIRE

    Poldrack, Russell A.; Halchenko, Yaroslav ,; Hanson, Stephen José

    2009-01-01

    Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes ar...

  14. A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS AND PERKS

    Directory of Open Access Journals (Sweden)

    Mitali Desai

    2016-03-01

    Full Text Available The social networking sites have brought a new horizon for expressing views and opinions of individuals. Moreover, they provide medium to students to share their sentiments including struggles and joy during the learning process. Such informal information has a great venue for decision making. The large and growing scale of information needs automatic classification techniques. Sentiment analysis is one of the automated techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful to draw decisions in education system since they classify the sentiments into merely three pre-set categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or negative category does not provide deeper insight into their problems and perks. In this paper, we propose a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several categories to help future students and education system in decision making.

  15. A Spiking Neural Learning Classifier System

    CERN Document Server

    Howard, Gerard; Lanzi, Pier-Luca

    2012-01-01

    Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together sequences of heterogeneous actions into macro-actions, it is shown to perform optimally in a problem where traditional methods can fail to find a solution in a reasonable amount of time. Our final system is tested on a simulated robotics platform.

  16. Learning Vector Quantization for Classifying Astronomical Objects

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    The sizes of astronomical surveys in different wavebands are increas-ing rapidly. Therefore, automatic classification of objects is becoming ever moreimportant. We explore the performance of learning vector quantization (LVQ) inclassifying multi-wavelength data. Our analysis concentrates on separating activesources from non-active ones. Different classes of X-ray emitters populate distinctregions of a multidimensional parameter space. In order to explore the distributionof various objects in a multidimensional parameter space, we positionally cross-correlate the data of quasars, BL Lacs, active galaxies, stars and normal galaxiesin the optical, X-ray and infrared bands. We then apply LVQ to classify them withthe obtained data. Our results show that LVQ is an effective method for separatingAGNs from stars and normal galaxies with multi-wavelength data.

  17. Automatic Fracture Detection Using Classifiers- A Review

    Directory of Open Access Journals (Sweden)

    S.K.Mahendran

    2011-11-01

    Full Text Available X-Ray is one the oldest and frequently used devices, that makes images of any bone in the body, including the hand, wrist, arm, elbow, shoulder, foot, ankle, leg (shin, knee, thigh, hip, pelvis or spine. A typical bone ailment is the fracture, which occurs when bone cannot withstand outside force like direct blows, twisting injuries and falls. Fractures are cracks in bones and are defined as a medical condition in which there is a break in the continuity of the bone. Detection and correct treatment of fractures are considered important, as a wrong diagnosis often lead to ineffective patient management, increased dissatisfaction and expensive litigation. The main focus of this paper is a review study that discusses about various classification algorithms that can be used to classify x-ray images as normal or fractured.

  18. Classifying gauge anomalies through SPT orders and classifying anomalies through topological orders

    CERN Document Server

    Wen, Xiao-Gang

    2013-01-01

    In this paper, we systematically study gauge anomalies in bosonic and fermionic weak-coupling gauge theories with gauge group G (which can be continuous or discrete). We argue that, in d space-time dimensions, the gauge anomalies are described by the elements in Free[H^{d+1}(G,R/Z)]\\oplus H_\\pi^{d+1}(BG,R/Z). The well known Adler-Bell-Jackiw anomalies are classified by the free part of the group cohomology class H^{d+1}(G,R/Z) of the gauge group G (denoted as Free[H^{d+1}(G,\\R/\\Z)]). We refer other kinds of gauge anomalies beyond Adler-Bell-Jackiw anomalies as nonABJ gauge anomalies, which include Witten SU(2) global gauge anomaly. We introduce a notion of \\pi-cohomology group, H_\\pi^{d+1}(BG,R/Z), for the classifying space BG, which is an Abelian group and include Tor[H^{d+1}(G,R/Z)] and topological cohomology group H^{d+1}(BG,\\R/\\Z) as subgroups. We argue that H_\\pi^{d+1}(BG,R/Z) classifies the bosonic nonABJ gauge anomalies, and partially classifies fermionic nonABJ anomalies. We also show a very close rel...

  19. 22 CFR 125.3 - Exports of classified technical data and classified defense articles.

    Science.gov (United States)

    2010-04-01

    ... classified defense articles. 125.3 Section 125.3 Foreign Relations DEPARTMENT OF STATE INTERNATIONAL TRAFFIC... commodity for shipment. A nontransfer and use certificate (Form DSP-83) executed by the applicant, foreign... reexport after a temporary import will be transferred or disclosed only in accordance with the...

  20. Gene-expression Classifier in Papillary Thyroid Carcinoma: Validation and Application of a Classifier for Prognostication

    DEFF Research Database (Denmark)

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

    2016-01-01

    frozen tissue from 38 patients was collected between the years 1986 and 2009. Validation cohort: formalin-fixed paraffin-embedded tissues were collected from 183 consecutively treated patients. RESULTS: A 17-gene classifier was identified based on the expression values in patients with and without...

  1. 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.

  2. Classifying gauge anomalies through symmetry-protected trivial orders and classifying gravitational anomalies through topological orders

    Science.gov (United States)

    Wen, Xiao-Gang

    2013-08-01

    In this paper, we systematically study gauge anomalies in bosonic and fermionic weak-coupling gauge theories with gauge group G (which can be continuous or discrete) in d space-time dimensions. We show a very close relation between gauge anomalies for gauge group G and symmetry-protected trivial (SPT) orders (also known as symmetry-protected topological (SPT) orders) with symmetry group G in one-higher dimension. The SPT phases are classified by group cohomology class Hd+1(G,R/Z). Through a more careful consideration, we argue that the gauge anomalies are described by the elements in Free[Hd+1(G,R/Z)]⊕Hπ˙d+1(BG,R/Z). The well known Adler-Bell-Jackiw anomalies are classified by the free part of Hd+1(G,R/Z) (denoted as Free[Hd+1(G,R/Z)]). We refer to other kinds of gauge anomalies beyond Adler-Bell-Jackiw anomalies as non-ABJ gauge anomalies, which include Witten SU(2) global gauge anomalies. We introduce a notion of π-cohomology group, Hπ˙d+1(BG,R/Z), for the classifying space BG, which is an Abelian group and include Tor[Hd+1(G,R/Z)] and topological cohomology group Hd+1(BG,R/Z) as subgroups. We argue that Hπ˙d+1(BG,R/Z) classifies the bosonic non-ABJ gauge anomalies and partially classifies fermionic non-ABJ anomalies. Using the same approach that shows gauge anomalies to be connected to SPT phases, we can also show that gravitational anomalies are connected to topological orders (i.e., patterns of long-range entanglement) in one-higher dimension.

  3. Classified Ads Harvesting Agent and Notification System

    CERN Document Server

    Doomun, Razvi; Nadeem, Auleear; Aukin, Mozafar

    2010-01-01

    The shift from an information society to a knowledge society require rapid information harvesting, reliable search and instantaneous on demand delivery. Information extraction agents are used to explore and collect data available from Web, in order to effectively exploit such data for business purposes, such as automatic news filtering, advertisement or product searching and price comparing. In this paper, we develop a real-time automatic harvesting agent for adverts posted on Servihoo web portal and an SMS-based notification system. It uses the URL of the web portal and the object model, i.e., the fields of interests and a set of rules written using the HTML parsing functions to extract latest adverts information. The extraction engine executes the extraction rules and stores the information in a database to be processed for automatic notification. This intelligent system helps to tremendously save time. It also enables users or potential product buyers to react more quickly to changes and newly posted sales...

  4. MISR Level 2 TOA/Cloud Classifier parameters V003

    Data.gov (United States)

    National Aeronautics and Space Administration — This is the Level 2 TOA/Cloud Classifiers Product. It contains the Angular Signature Cloud Mask (ASCM), Regional Cloud Classifiers, Cloud Shadow Mask, and...

  5. Optimized Radial Basis Function Classifier for Multi Modal Biometrics

    Directory of Open Access Journals (Sweden)

    Anand Viswanathan

    2014-07-01

    Full Text Available Biometric systems can be used for the identification or verification of humans based on their physiological or behavioral features. In these systems the biometric characteristics such as fingerprints, palm-print, iris or speech can be recorded and are compared with the samples for the identification or verification. Multimodal biometrics is more accurate and solves spoof attacks than the single modal bio metrics systems. In this study, a multimodal biometric system using fingerprint images and finger-vein patterns is proposed and also an optimized Radial Basis Function (RBF kernel classifier is proposed to identify the authorized users. The extracted features from these modalities are selected by PCA and kernel PCA and combined to classify by RBF classifier. The parameters of RBF classifier is optimized by using BAT algorithm with local search. The performance of the proposed classifier is compared with the KNN classifier, Naïve Bayesian classifier and non-optimized RBF classifier.

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

    CERN Document Server

    Rusconi, Marco

    2016-01-01

    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...

  7. Stress fracture development classified by bone scintigraphy

    International Nuclear Information System (INIS)

    There is no consensus on classifying stress fractures (SF) appearing on bone scans. The authors present a system of classification based on grading the severity and development of bone lesions by visual inspection, according to three main scintigraphic criteria: focality and size, intensity of uptake compare to adjacent bone, and local medular extension. Four grades of development (I-IV) were ranked, ranging from ill defined slightly increased cortical uptake to well defined regions with markedly increased uptake extending transversely bicortically. 310 male subjects aged 19-2, suffering several weeks from leg pains occurring during intensive physical training underwent bone scans of the pelvis and lower extremities using Tc-99-m-MDP. 76% of the scans were positive with 354 lesions, of which 88% were in th4e mild (I-II) grades and 12% in the moderate (III) and severe (IV) grades. Post-treatment scans were obtained in 65 cases having 78 lesions during 1- to 6-month intervals. Complete resolution was found after 1-2 months in 36% of the mild lesions but in only 12% of the moderate and severe ones, and after 3-6 months in 55% of the mild lesions and 15% of the severe ones. 75% of the moderate and severe lesions showed residual uptake in various stages throughout the follow-up period. Early recognition and treatment of mild SF lesions in this study prevented protracted disability and progression of the lesions and facilitated complete healing

  8. Classifying auroras using artificial neural networks

    Science.gov (United States)

    Rydesater, Peter; Brandstrom, Urban; Steen, Ake; Gustavsson, Bjorn

    1999-03-01

    In Auroral Large Imaging System (ALIS) there is need of stable methods for analysis and classification of auroral images and images with for example mother of pearl clouds. This part of ALIS is called Selective Imaging Techniques (SIT) and is intended to sort out images of scientific interest. It's also used to find out what and where in the images there is for example different auroral phenomena's. We will discuss some about the SIT units main functionality but this work is mainly concentrated on how to find auroral arcs and how they are placed in images. Special case have been taken to make the algorithm robust since it's going to be implemented in a SIT unit which will work automatic and often unsupervised and some extends control the data taking of ALIS. The method for finding auroral arcs is based on a local operator that detects intensity differens. This gives arc orientation values as a preprocessing which is fed to a neural network classifier. We will show some preliminary results and possibilities to use and improve this algorithm for use in the future SIT unit.

  9. Colorization by classifying the prior knowledge

    Institute of Scientific and Technical Information of China (English)

    DU Weiwei

    2011-01-01

    When a one-dimensional luminance scalar is replaced by a vector of a colorful multi-dimension for every pixel of a monochrome image,the process is called colorization.However,colorization is under-constrained.Therefore,the prior knowledge is considered and given to the monochrome image.Colorization using optimization algorithm is an effective algorithm for the above problem.However,it cannot effectively do with some images well without repeating experiments for confirming the place of scribbles.In this paper,a colorization algorithm is proposed,which can automatically generate the prior knowledge.The idea is that firstly,the prior knowledge crystallizes into some points of the prior knowledge which is automatically extracted by downsampling and upsampling method.And then some points of the prior knowledge are classified and given with corresponding colors.Lastly,the color image can be obtained by the color points of the prior knowledge.It is demonstrated that the proposal can not only effectively generate the prior knowledge but also colorize the monochrome image according to requirements of user with some experiments.

  10. Application of Multidimensional Chain classifiers to Eddy Current Images for Defect Characterization

    Directory of Open Access Journals (Sweden)

    S. Shuaib Ahmed

    2012-12-01

    Full Text Available Multidimensional learning problem deals with learning a function that maps a vector of input features to a vector of class labels. Dependency between the classes is not taken into account while constructing independent classifiers for each component class of vector. To counteract this limitation, Chain Classifiers (CC approach for multidimensional learning is proposed in this study. In this approach, the information of class dependency is passed along a chain. Radial Basis Functions (RBF and Support Vector Machines (SVM are used as core for CC. Studies on multidimensional dataset of images obtained from simulated eddy current non-destructive evaluation of a stainless steel plate with sub-surface defects clearly indicate that the performance of the chain classifier is superior to the independent classifiers.

  11. Higher School Marketing Strategy Formation: Classifying the Factors

    Directory of Open Access Journals (Sweden)

    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. 

  12. Prediction of Pork Quality by Fuzzy Support Vector Machine Classifier

    Science.gov (United States)

    Zhang, Jianxi; Yu, Huaizhi; Wang, Jiamin

    Existing objective methods to evaluate pork quality in general do not yield satisfactory results and their applications in meat industry are limited. In this study, fuzzy support vector machine (FSVM) method was developed to evaluate and predict pork quality rapidly and nondestructively. Firstly, the discrete wavelet transform (DWT) was used to eliminate the noise component in original spectrum and the new spectrum was reconstructed. Then, considering the characteristic variables still exist correlation and contain some redundant information, principal component analysis (PCA) was carried out. Lastly, FSVM was developed to differentiate and classify pork samples into different quality grades using the features from PCA. Jackknife tests on the working datasets indicated that the prediction accuracies were higher than other methods.

  13. A new machine learning classifier for high dimensional healthcare data.

    Science.gov (United States)

    Padman, Rema; Bai, Xue; Airoldi, Edoardo M

    2007-01-01

    Data sets with many discrete variables and relatively few cases arise in health care, commerce, information security, and many other domains. Learning effective and efficient prediction models from such data sets is a challenging task. In this paper, we propose a new approach that combines Metaheuristic search and Bayesian Networks to learn a graphical Markov Blanket-based classifier from data. The Tabu Search enhanced Markov Blanket (TS/MB) procedure is based on the use of restricted neighborhoods in a general Bayesian Network constrained by the Markov condition, called Markov Blanket Neighborhoods. Computational results from two real world healthcare data sets indicate that the TS/MB procedure converges fast and is able to find a parsimonious model with substantially fewer predictor variables than in the full data set. Furthermore, it has comparable or better prediction performance when compared against several machine learning methods, and provides insight into possible causal relations among the variables. PMID:17911800

  14. Combining classifiers for robust PICO element detection

    OpenAIRE

    Grad Roland; Bartlett Joan C; Nie Jian-Yun; Boudin Florian; Pluye Pierre; Dawes Martin

    2010-01-01

    Abstract Background Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents. Methods In ...

  15. 78 FR 5116 - NASA Information Security Protection

    Science.gov (United States)

    2013-01-24

    ... SPACE ADMINISTRATION 14 CFR Part 1203 RIN 2700-AD61 NASA Information Security Protection AGENCY..., Classified National Security Information, and appropriately to correspond with NASA's internal requirements, NPR 1600.2, Classified National Security Information, that establishes the Agency's requirements...

  16. Classifying rock lithofacies using petrophysical data

    Science.gov (United States)

    Al-Omair, Osamah; Garrouch, Ali A.

    2010-09-01

    This study automates a type-curve technique for estimating the rock pore-geometric factor (λ) from capillary pressure measurements. The pore-geometric factor is determined by matching the actual rock capillary pressure versus wetting-phase saturation (Pc-Sw) profile with that obtained from the Brooks and Corey model (1966 J. Irrigation Drainage Proc. Am. Soc. Civ. Eng. 61-88). The pore-geometric factor values are validated by comparing the actual measured rock permeability to the permeability values estimated using the Wyllie and Gardner model (1958 World Oil (April issue) 210-28). Petrophysical data for both carbonate and sandstone rocks, along with the pore-geometric factor derived from the type-curve matching, are used in a discriminant analysis for the purpose of developing a model for rock typing. The petrophysical parameters include rock porosity (phi), irreducible water saturation (Swi), permeability (k), the threshold capillary-entry-pressure (Pd), a pore-shape factor (β), and a flow-impedance parameter (n) which is a property that reflects the flow impedance caused by the irreducible wetting-phase saturation. The results of the discriminant analysis indicate that five of the parameters (phi, k, Pd, λ and n) are sufficient for classifying rocks according to two broad lithology classes: sandstones and carbonates. The analysis reveals the existence of a significant discriminant function that is mostly sensitive to the pore-geometric factor values (λ). A discriminant-analysis classification model that honours both static and dynamic petrophysical rock properties is, therefore, introduced. When tested on two distinct data sets, the discriminant-analysis model was able to predict the correct lithofacies for approximately 95% of the tested samples. A comprehensive database of the experimentally collected petrophysical properties of 215 carbonate and sandstone rocks is provided with this study.

  17. 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.

  18. A new approach to classifier fusion based on upper integral.

    Science.gov (United States)

    Wang, Xi-Zhao; Wang, Ran; Feng, Hui-Min; Wang, Hua-Chao

    2014-05-01

    Fusing a number of classifiers can generally improve the performance of individual classifiers, and the fuzzy integral, which can clearly express the interaction among the individual classifiers, has been acknowledged as an effective tool of fusion. In order to make the best use of the individual classifiers and their combinations, we propose in this paper a new scheme of classifier fusion based on upper integrals, which differs from all the existing models. Instead of being a fusion operator, the upper integral is used to reasonably arrange the finite resources, and thus to maximize the classification efficiency. By solving an optimization problem of upper integrals, we obtain a scheme for assigning proportions of examples to different individual classifiers and their combinations. According to these proportions, new examples could be classified by different individual classifiers and their combinations, and the combination of classifiers that specific examples should be submitted to depends on their performance. The definition of upper integral guarantees such a conclusion that the classification efficiency of the fused classifier is not less than that of any individual classifier theoretically. Furthermore, numerical simulations demonstrate that most existing fusion methodologies, such as bagging and boosting, can be improved by our upper integral model. PMID:23782843

  19. Face Detection Using a First-Order RCE Classifier

    Directory of Open Access Journals (Sweden)

    Byeong Hwan Jeon

    2003-08-01

    Full Text Available We present a new face detection algorithm based on a first-order reduced Coulomb energy (RCE classifier. The algorithm locates frontal views of human faces at any degree of rotation and scale in complex scenes. The face candidates and their orientations are first determined by computing the Hausdorff distance between simple face abstraction models and binary test windows in an image pyramid. Then, after normalizing the energy, each face candidate is verified by two subsequent classifiers: a binary image classifier and the first-order RCE classifier. While the binary image classifier is employed as a preclassifier to discard nonfaces with minimum computational complexity, the first-order RCE classifier is used as the main face classifier for final verification. An optimal training method to construct the representative face model database is also presented. Experimental results show that the proposed algorithm yields a high detection ratio while yielding no false alarm.

  20. Information barriers and authentication

    International Nuclear Information System (INIS)

    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.

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

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2008-11-01

    Full Text Available 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 statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA. In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP and support vector classifier (SVC are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.

  2. Rule Based Ensembles Using Pair Wise Neural Network Classifiers

    Directory of Open Access Journals (Sweden)

    Moslem Mohammadi Jenghara

    2015-03-01

    Full Text Available In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks that are investigated in two aspects. In the first aspect, feature space is divided into several local regions and each region is assigned with a highly competent classifier and in the second, the base classifiers are applied in parallel and equally experienced in some ways to achieve a group consensus. In this paper combination of two methods are used. An important consideration in classifier combination is that much better results can be achieved if diverse classifiers, rather than similar classifiers, are combined. To achieve diversity in classifiers output, the symmetric pairwise weighted feature space is used and the outputs of trained classifiers over the weighted feature space are combined to inference final result. In this paper MLP classifiers are used as the base classifiers. The Experimental results show that the applied method is promising.

  3. Adaboost Ensemble Classifiers for Corporate Default Prediction

    OpenAIRE

    Suresh Ramakrishnan; Maryam Mirzaei; Mahmoud Bekri

    2015-01-01

    This study aims to show a substitute technique to corporate default prediction. Data mining techniques have been extensively applied for this task, due to its ability to notice non-linear relationships and show a good performance in presence of noisy information, as it usually happens in corporate default prediction problems. In spite of several progressive methods that have widely been proposed, this area of research is not out dated and still needs further examination. In this study, the pe...

  4. 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.

  5. 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.

  6. Classifying Volcanic Activity Using an Empirical Decision Making Algorithm

    Science.gov (United States)

    Junek, W. N.; Jones, W. L.; Woods, M. T.

    2012-12-01

    Detection and classification of developing volcanic activity is vital to eruption forecasting. Timely information regarding an impending eruption would aid civil authorities in determining the proper response to a developing crisis. In this presentation, volcanic activity is characterized using an event tree classifier and a suite of empirical statistical models derived through logistic regression. Forecasts are reported in terms of the United States Geological Survey (USGS) volcano alert level system. The algorithm employs multidisciplinary data (e.g., seismic, GPS, InSAR) acquired by various volcano monitoring systems and source modeling information to forecast the likelihood that an eruption, with a volcanic explosivity index (VEI) > 1, will occur within a quantitatively constrained area. Logistic models are constructed from a sparse and geographically diverse dataset assembled from a collection of historic volcanic unrest episodes. Bootstrapping techniques are applied to the training data to allow for the estimation of robust logistic model coefficients. Cross validation produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78-0.81, which indicates the algorithm has good predictive capabilities. The ROC curves also allowed for the determination of a false positive rate and optimum detection for each stage of the algorithm. Forecasts for historic volcanic unrest episodes in North America and Iceland were computed and are consistent with the actual outcome of the events.

  7. Learning a Flexible K-Dependence Bayesian Classifier from the Chain Rule of Joint Probability Distribution

    Directory of Open Access Journals (Sweden)

    Limin Wang

    2015-06-01

    Full Text Available As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity. The scoring functions once proposed and widely used for a Bayesian network are not appropriate for a Bayesian classifier, in which class variable C is considered as a distinguished one. In this paper, we aim to clarify the working mechanism of Bayesian classifiers from the perspective of the chain rule of joint probability distribution. By establishing the mapping relationship between conditional probability distribution and mutual information, a new scoring function, Sum_MI, is derived and applied to evaluate the rationality of the Bayesian classifiers. To achieve global optimization and high dependence representation, the proposed learning algorithm, the flexible K-dependence Bayesian (FKDB classifier, applies greedy search to extract more information from the K-dependence network structure. Meanwhile, during the learning procedure, the optimal attribute order is determined dynamically, rather than rigidly. In the experimental study, functional dependency analysis is used to improve model interpretability when the structure complexity is restricted.

  8. To fuse or not to fuse: Fuser versus best classifier

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.

    1998-04-01

    A sample from a class defined on a finite-dimensional Euclidean space and distributed according to an unknown distribution is given. The authors are given a set of classifiers each of which chooses a hypothesis with least misclassification error from a family of hypotheses. They address the question of choosing the classifier with the best performance guarantee versus combining the classifiers using a fuser. They first describe a fusion method based on isolation property such that the performance guarantee of the fused system is at least as good as the best of the classifiers. For a more restricted case of deterministic classes, they present a method based on error set estimation such that the performance guarantee of fusing all classifiers is at least as good as that of fusing any subset of classifiers.

  9. Taxonomy grounded aggregation of classifiers with different label sets

    OpenAIRE

    SAHA, AMRITA; Indurthi, Sathish; Godbole, Shantanu; Rongali, Subendhu; Raykar, Vikas C.

    2015-01-01

    We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy. Such a taxonomy may not have been available or referenced when the individual classifiers were designed and trained, yet mapping the output labels into the taxonomy is desirable to integrate the effort spent in training the constituent classifiers. A hierarchical taxonomy representing some domain knowledge may be different from, but partially mappable to, the label sets of the individua...

  10. Investigating The Fusion of Classifiers Designed Under Different Bayes Errors

    Directory of Open Access Journals (Sweden)

    Fuad M. Alkoot

    2004-12-01

    Full Text Available We investigate a number of parameters commonly affecting the design of a multiple classifier system in order to find when fusing is most beneficial. We extend our previous investigation to the case where unequal classifiers are combined. Results indicate that Sum is not affected by this parameter, however, Vote degrades when a weaker classifier is introduced in the combining system. This is more obvious when estimation error with uniform distribution exists.

  11. The SVM Classifier Based on the Modified Particle Swarm Optimization

    OpenAIRE

    Liliya Demidova; Evgeny Nikulchev; Yulia Sokolova

    2016-01-01

    The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particles' {\\guillemotleft}regeneration{\\guillemotright} is put on the basis of the modified particle swarm ...

  12. The analysis of cross-classified categorical data

    CERN Document Server

    Fienberg, Stephen E

    2007-01-01

    A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation.

  13. Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels

    CERN Document Server

    Donmez, Pinar; Lebanon, Guy

    2010-01-01

    Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and p(y). We prove that the technique is consistent for high-dimensional linear classifiers and demonstrate it on synthetic and real-world data. In particular, we show how the estimate is used for evaluating classifiers in transfer learning, and for training classifiers with no labeled data whatsoever.

  14. Construction of unsupervised sentiment classifier on idioms resources

    Institute of Scientific and Technical Information of China (English)

    谢松县; 王挺

    2014-01-01

    Sentiment analysis is the computational study of how opinions, attitudes, emotions, and perspectives are expressed in language, and has been the important task of natural language processing. Sentiment analysis is highly valuable for both research and practical applications. The focuses were put on the difficulties in the construction of sentiment classifiers which normally need tremendous labeled domain training data, and a novel unsupervised framework was proposed to make use of the Chinese idiom resources to develop a general sentiment classifier. Furthermore, the domain adaption of general sentiment classifier was improved by taking the general classifier as the base of a self-training procedure to get a domain self-training sentiment classifier. To validate the effect of the unsupervised framework, several experiments were carried out on publicly available Chinese online reviews dataset. The experiments show that the proposed framework is effective and achieves encouraging results. Specifically, the general classifier outperforms two baselines (a Naïve 50% baseline and a cross-domain classifier), and the bootstrapping self-training classifier approximates the upper bound domain-specific classifier with the lowest accuracy of 81.5%, but the performance is more stable and the framework needs no labeled training dataset.

  15. Facial expression recognition with facial parts based sparse representation classifier

    Science.gov (United States)

    Zhi, Ruicong; Ruan, Qiuqi

    2009-10-01

    Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.

  16. Using Classifiers to Identify Binge Drinkers Based on Drinking Motives.

    Science.gov (United States)

    Crutzen, Rik; Giabbanelli, Philippe

    2013-08-21

    A representative sample of 2,844 Dutch adult drinkers completed a questionnaire on drinking motives and drinking behavior in January 2011. Results were classified using regressions, decision trees, and support vector machines (SVMs). Using SVMs, the mean absolute error was minimal, whereas performance on identifying binge drinkers was high. Moreover, when comparing the structure of classifiers, there were differences in which drinking motives contribute to the performance of classifiers. Thus, classifiers are worthwhile to be used in research regarding (addictive) behaviors, because they contribute to explaining behavior and they can give different insights from more traditional data analytical approaches. PMID:23964957

  17. Mesh Learning for Classifying Cognitive Processes

    CERN Document Server

    Ozay, Mete; Öztekin, Uygar; Vural, Fatos T Yarman

    2012-01-01

    The major goal of this study is to model the encoding and retrieval operations of the brain during memory processing, using statistical learning tools. The suggested method assumes that the memory encoding and retrieval processes can be represented by a supervised learning system, which is trained by the brain data collected from the functional Magnetic Resonance (fMRI) measurements, during the encoding stage. Then, the system outputs the same class labels as that of the fMRI data collected during the retrieval stage. The most challenging problem of modeling such a learning system is the design of the interactions among the voxels to extract the information about the underlying patterns of brain activity. In this study, we suggest a new method called Mesh Learning, which represents each voxel by a mesh of voxels in a neighborhood system. The nodes of the mesh are a set of neighboring voxels, whereas the arc weights are estimated by a linear regression model. The estimated arc weights are used to form Local Re...

  18. 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

  19. Self-recalibrating classifiers for intracortical brain-computer interfaces

    Science.gov (United States)

    Bishop, William; Chestek, Cynthia C.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2014-04-01

    Objective. Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach. We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results. We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a \\mathord {\\sim }15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance. We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.

  20. 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...

  1. 40 CFR 152.175 - Pesticides classified for restricted use.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Pesticides classified for restricted...) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.175 Pesticides classified for restricted use. The following uses of pesticide products containing...

  2. Genetic Algorithm Based Incremental Learning For Optimal Weight and Classifier Selection

    Science.gov (United States)

    Hulley, Gregory; Marwala, Tshilidzi

    2007-11-01

    The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has shown good incremental learning capabilities on benchmark datasets on which the new ILUGA method has been tested. ILUGA has also shown good incremental learning ability using only a few classifiers and does not suffer from catastrophic forgetting. The results obtained for ILUGA on the Optical Character Recognition (OCR) and Wine datasets are good, with an overall accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT for the difficult multi-class OCR dataset.

  3. Analysis of Sequence Based Classifier Prediction for HIV Subtypes

    Directory of Open Access Journals (Sweden)

    S. Santhosh Kumar

    2012-10-01

    Full Text Available Human immunodeficiency virus (HIV is a lent virus that causes acquired immunodeficiency syndrome (AIDS. The main drawback in HIV treatment process is its sub type prediction. The sub type and group classification of HIV is based on its genetic variability and location. HIV can be divided into two major types, HIV type 1 (HIV-1 and HIV type 2 (HIV-2. Many classifier approaches have been used to classify HIV subtypes based on their group, but some of cases are having two groups in one; in such cases the classification becomes more complex. The methodology used is this paper based on the HIV sequences. For this work several classifier approaches are used to classify the HIV1 and HIV2. For implementation of the work a real time patient database is taken and the patient records are experimented and the final best classifier is identified with quick response time and least error rate.

  4. Algorithm for classifying multiple targets using acoustic signatures

    Science.gov (United States)

    Damarla, Thyagaraju; Pham, Tien; Lake, Douglas

    2004-08-01

    In this paper we discuss an algorithm for classification and identification of multiple targets using acoustic signatures. We use a Multi-Variate Gaussian (MVG) classifier for classifying individual targets based on the relative amplitudes of the extracted harmonic set of frequencies. The classifier is trained on high signal-to-noise ratio data for individual targets. In order to classify and further identify each target in a multi-target environment (e.g., a convoy), we first perform bearing tracking and data association. Once the bearings of the targets present are established, we next beamform in the direction of each individual target to spatially isolate it from the other targets (or interferers). Then, we further process and extract a harmonic feature set from each beamformed output. Finally, we apply the MVG classifier on each harmonic feature set for vehicle classification and identification. We present classification/identification results for convoys of three to five ground vehicles.

  5. An ensemble of dissimilarity based classifiers for Mackerel gender determination

    International Nuclear Information System (INIS)

    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

  6. 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.

  7. Manipulating neural activity in physiologically classified neurons: triumphs and challenges.

    Science.gov (United States)

    Gore, Felicity; Schwartz, Edmund C; Salzman, C Daniel

    2015-09-19

    Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour. PMID:26240431

  8. Fuzzy-Genetic Classifier algorithm for bank's customers

    Directory of Open Access Journals (Sweden)

    Rashed Mokhtar Elawady

    2011-09-01

    Full Text Available Modern finical banks are running in complex and dynamic environment which may bring high uncertainty and risk to them. So the ability to intelligently collect, mange, and analyze information about customers is a key source of competitive advantage for an E-business. But the data base for any bank is too large, complex and incomprehensible to determine if the customer risk or default. This paper presents a new algorithm for extracting accurate and comprehensible rules from database via fuzzy genetic classifier by two methodologies fuzzy system and genetic algorithms in one algorithm. Proposed evolved system exhibits two important characteristics; first, each rule is obtained through an efficient genetic rule extraction method which adapts the parameters of the fuzzy sets in the premise space and determines the required features of the rule, further improve the interpretability of the obtained model. Second, evolve the obtained rule base through genetic algorithm. The cooperation system increases the classification performance and reach to max classification ratio in the earlier generations.

  9. Construction of High-accuracy Ensemble of Classifiers

    Directory of Open Access Journals (Sweden)

    Hedieh Sajedi

    2014-04-01

    Full Text Available There have been several methods developed to construct ensembles. Some of these methods, such as Bagging and Boosting are meta-learners, i.e. they can be applied to any base classifier. The combination of methods should be selected in order that classifiers cover each other weaknesses. In ensemble, the output of several classifiers is used only when they disagree on some inputs. The degree of disagreement is called diversity of the ensemble. Another factor that plays a significant role in performing an ensemble is accuracy of the basic classifiers. It can be said that all the procedures of constructing ensembles seek to achieve a balance between these two parameters, and successful methods can reach a better balance. The diversity of the members of an ensemble is known as an important factor in determining its generalization error. In this paper, we present a new approach for generating ensembles. The proposed approach uses Bagging and Boosting as the generators of base classifiers. Subsequently, the classifiers are partitioned by means of a clustering algorithm. We introduce a selection phase for construction the final ensemble and three different selection methods are proposed for applying in this phase. In the first proposed selection method, a classifier is selected randomly from each cluster. The second method selects the most accurate classifier from each cluster and the third one selects the nearest classifier to the center of each cluster to construct the final ensemble. The results of the experiments on well-known datasets demonstrate the strength of our proposed approach, especially applying the selection of the most accurate classifiers from clusters and employing Bagging generator.

  10. PERFORMANCE EVALUATION OF VARIOUS STATISTICAL CLASSIFIERS IN DETECTING THE DISEASED CITRUS LEAVES

    Directory of Open Access Journals (Sweden)

    SUDHEER REDDY BANDI

    2013-02-01

    Full Text Available Citrus fruits are in lofty obligation because the humans consume them daily. This research aims to amend citrus production, which knows a low upshot bourgeois on the production and complex during measurements. Nowadays citrus plants grappling some traits/diseases. Harm of the insect is one of the major trait/disease. Insecticides are not ever evidenced effectual because insecticides may be toxic to some gracious of birds. Farmers get outstanding difficulties in detecting the diseases ended open eye and also it is quite expensive.Machine vision and Image processing techniques helps in sleuthing the disease mark in citrus leaves and sound job. In this search, Citrus leaves of four classes like Normal, Greasy spot, Melanose and Scab are collected and investigated using texture analysis based on the Color Co-occurrence Method (CCM to take Hue, Saturation and Intensity (HSI features. In the arrangement form, the features are categorised for all leafage conditions using k-Nearest Neighbor (kNN, Naive Bayes classifier (NBC, Linear Discriminate Analysis (LDA classifier and Random Forest Tree Algorithm classifier (RFT. The experimental results inform that proposed attack significantly supports 98.75% quality in automated detection of regular and struck leaves using texture psychotherapy based CCM method using LDA formula. Eventually all the classifiers are compared using Earphone Operative Characteristic contour and analyzed the performance of all the classifiers.

  11. Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers

    Directory of Open Access Journals (Sweden)

    Jyoti soni,

    2011-06-01

    Full Text Available The healthcare environment is still ‘information rich’ But ‘knowledge poor’. There is a wealth of data available within the health care systems. However, there is a lack of effective analysis tools todiscover hidden relationships in data. The aim of this work is to design a GUI based Interface to enter the patient record and predict whether the patient is having Heart disease or not using Weighted Association rule based Classifier. The prediction is performed from mining the patient’s historical data or data repository. In Weighted Associative Classifier (WAC, different weights are assigned to different attributes according to their predicting capability. It has already been proved that the Associative Classifiers are performing well than traditional classifiers approaches such as decision tree and rule induction. Further from experimental results it has been found that WAC is providing improved accuracy as compare to other already existing Associative Classifiers. Hence the system is using WAC as a Data mining technique to generate rule base. The system has been implemented in java Platform and trained using benchmark data from UCI machine learning repository. The system is expandable for thenew dataset.

  12. Evaluating and classifying the readiness of technology specifications for national standardization.

    Science.gov (United States)

    Baker, Dixie B; Perlin, Jonathan B; Halamka, John

    2015-05-01

    The American Recovery and Reinvestment Act (ARRA) of 2009 clearly articulated the central role that health information technology (HIT) standards would play in improving healthcare quality, safety, and efficiency through the meaningful use of certified, standards based, electronic health record (EHR) technology. In 2012, the Office of the National Coordinator (ONC) asked the Nationwide Health Information Network (NwHIN) Power Team of the Health Information Technology Standards Committee (HITSC) to develop comprehensive, objective, and, to the extent practical, quantitative criteria for evaluating technical standards and implementation specifications and classifying their readiness for national adoption. The Power Team defined criteria, attributes, and metrics for evaluating and classifying technical standards and specifications as 'emerging,' 'pilot,' or 'ready for national standardization' based on their maturity and adoptability. The ONC and the HITSC are now using these metrics for assessing the readiness of technical standards for national adoption. PMID:24872342

  13. Classifying transcription factor targets and discovering relevant biological features

    Directory of Open Access Journals (Sweden)

    DeLisi Charles

    2008-05-01

    Full Text Available Abstract Background An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties. Principal Findings (1 Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4, Ino2(2.6, Yaf1(2.4, and Yap6(2.4. (2 Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3 A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4 An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and

  14. 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.

  15. Malignancy and Abnormality Detection of Mammograms using Classifier Ensembling

    Directory of Open Access Journals (Sweden)

    Nawazish Naveed

    2011-07-01

    Full Text Available The breast cancer detection and diagnosis is a critical and complex procedure that demands high degree of accuracy. In computer aided diagnostic systems, the breast cancer detection is a two stage procedure. First, to classify the malignant and benign mammograms, while in second stage, the type of abnormality is detected. In this paper, we have developed a novel architecture to enhance the classification of malignant and benign mammograms using multi-classification of malignant mammograms into six abnormality classes. DWT (Discrete Wavelet Transformation features are extracted from preprocessed images and passed through different classifiers. To improve accuracy, results generated by various classifiers are ensembled. The genetic algorithm is used to find optimal weights rather than assigning weights to the results of classifiers on the basis of heuristics. The mammograms declared as malignant by ensemble classifiers are divided into six classes. The ensemble classifiers are further used for multiclassification using one-against-all technique for classification. The output of all ensemble classifiers is combined by product, median and mean rule. It has been observed that the accuracy of classification of abnormalities is more than 97% in case of mean rule. The Mammographic Image Analysis Society dataset is used for experimentation.

  16. A Comparison of Unsupervised Classifiers on BATSE Catalog Data

    Science.gov (United States)

    Hakkila, Jon; Roiger, Richard J.; Haglin, David J.; Giblin, Timothy W.; Paciesas, William S.

    2003-04-01

    We classify BATSE gamma-ray bursts using unsupervised clustering algorithms in order to compare classification with statistical clustering techniques. BATSE bursts detected with homogeneous trigger criteria and measured with a limited attribute set (duration, hardness, and fluence) are classified using four unsupervised algorithms (the concept hierarchy classifier ESX, the EM algorithm, the Kmeans algorithm, and a kohonen neural network). The classifiers prefer three-class solutions to two-class and four-class solutions. When forced to find two classes, the classifiers do not find the traditional long and short classes; many short soft events are placed in a class with the short hard bursts. When three classes are found, the classifiers clearly identify the short bursts, but place far more members in an intermediate duration soft class than have been found using statistical clustering techniques. It appears that the boundary between short faint and long bright bursts is more important to the classifiers than is the boundary between short hard and long soft bursts. We conclude that the boundary between short faint and long hard bursts is the result of data bias and poor attribute selection. We recommend that future gamma-ray burst classification avoid using extrinsic parameters such as fluence, and should instead concentrate on intrinsic properties such as spectral, temporal, and (when available) luminosity characteristics. Future classification should also be wary of correlated attributes (such as fluence and duration), as these bias classification results.

  17. Classifying Response Correctness across Different Task Sets: A Machine Learning Approach.

    Science.gov (United States)

    Plewan, Thorsten; Wascher, Edmund; Falkenstein, Michael; Hoffmann, Sven

    2016-01-01

    Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms (EEG) typically reveals a negative potential at fronto-central electrodes shortly following a response error (Ne or ERN) as well as an error-awareness-related positivity (Pe). Seemingly, the brain signal contains information about the occurrence of an error. Assuming a general error evaluation system, the question arises whether this information can be utilized in order to classify behavioral performance within or even across different cognitive tasks. In the present study, a machine learning approach was employed to investigate the outlined issue. Ne as well as Pe were extracted from the single-trial EEG signals of participants conducting a flanker and a mental rotation task and subjected to a machine learning classification scheme (via a support vector machine, SVM). Overall, individual performance in the flanker task was classified more accurately, with accuracy rates of above 85%. Most importantly, it was even feasible to classify responses across both tasks. In particular, an SVM trained on the flanker task could identify erroneous behavior with almost 70% accuracy in the EEG data recorded during the rotation task, and vice versa. Summed up, we replicate that the response-related EEG signal can be used to identify erroneous behavior within a particular task. Going beyond this, it was possible to classify response types across functionally different tasks. Therefore, the outlined methodological approach appears promising with respect to future applications. PMID:27032108

  18. Use of Mamdani-Assilian Fuzzy Controller for Combining Classifiers

    Czech Academy of Sciences Publication Activity Database

    Štefka, David; Holeňa, Martin

    Praha : Matfyzpress, 2007 - (Obdržálek, D.; Štanclová, J.; Plátek, M.), s. 88-97 ISBN 978-80-7378-033-3. [ MIS 2007. Malý informatický seminář /24./. Josefův důl (CZ), 13.01.2007-20.01.2007] R&D Projects: GA AV ČR 1ET100300517; GA ČR GA201/05/0325 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy control * classifier fusion * classifier aggregation * classifier combining Subject RIV: IN - Informatics, Computer Science

  19. Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP.

    Science.gov (United States)

    Roijendijk, Linsey; Gielen, Stan; Farquhar, Jason

    2016-08-01

    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 contains two supervised learning stages: the first in which class- relevant spatial filters are learned and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters. PMID:26372428

  20. Information

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    There are unstructured abstracts (no more than 256 words) and structured abstracts (no more than 480). The specific requirements for structured abstracts are as follows:An informative, structured abstracts of no more than 4-80 words should accompany each manuscript. Abstracts for original contributions should be structured into the following sections. AIM (no more than 20 words): Only the purpose should be included. Please write the aim as the form of "To investigate/ study/..."; MATERIALS AND METHODS (no more than 140 words); RESULTS (no more than 294 words): You should present P values where appropnate and must provide relevant data to illustrate how they were obtained, e.g. 6.92 ± 3.86 vs 3.61 ± 1.67, P< 0.001; CONCLUSION (no more than 26 words).

  1. From Informational Confidence to Informational Intelligence

    OpenAIRE

    Jaeger, Stefan

    2006-01-01

    This paper is a continuation of my previous work on informational confidence. The main idea of this technique is to normalize confidence values from different sources in such a way that they match their informational content determined by their performance in an application domain. This reduces classifier combination to a simple integration of information. The proposed method has shown good results in handwriting recognition and other applications involving classifier combination. In the pres...

  2. Classifier performance estimation under the constraint of a finite sample size: resampling schemes applied to neural network classifiers.

    Science.gov (United States)

    Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

    2008-01-01

    In a practical classifier design problem the sample size is limited, and the available finite sample needs to be used both to design a classifier and to predict the classifier's performance for the true population. Since a larger sample is more representative of the population, it is advantageous to design the classifier with all the available cases, and to use a resampling technique for performance prediction. We conducted a Monte Carlo simulation study to compare the ability of different resampling techniques in predicting the performance of a neural network (NN) classifier designed with the available sample. We used the area under the receiver operating characteristic curve as the performance index for the NN classifier. We investigated resampling techniques based on the cross-validation, the leave-one-out method, and three different types of bootstrapping, namely, the ordinary, .632, and .632+ bootstrap. Our results indicated that, under the study conditions, there can be a large difference in the accuracy of the prediction obtained from different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited data set. PMID:18234468

  3. A NON-PARAMETER BAYESIAN CLASSIFIER FOR FACE RECOGNITION

    Institute of Scientific and Technical Information of China (English)

    Liu Qingshan; Lu Hanqing; Ma Songde

    2003-01-01

    A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.

  4. Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS - R code

    OpenAIRE

    Irawan, Dasapta Erwin; Gio, Prana Ugiana

    2016-01-01

    The following R code was used in this paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS" authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1

  5. 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

  6. Which Is Better: Holdout or Full-Sample Classifier Design?

    Directory of Open Access Journals (Sweden)

    Edward R. Dougherty

    2008-04-01

    Full Text Available Is it better to design a classifier and estimate its error on the full sample or to design a classifier on a training subset and estimate its error on the holdout test subset? Full-sample design provides the better classifier; nevertheless, one might choose holdout with the hope of better error estimation. A conservative criterion to decide the best course is to aim at a classifier whose error is less than a given bound. Then the choice between full-sample and holdout designs depends on which possesses the smaller expected bound. Using this criterion, we examine the choice between holdout and several full-sample error estimators using covariance models and a patient-data model. Full-sample design consistently outperforms holdout design. The relation between the two designs is revealed via a decomposition of the expected bound into the sum of the expected true error and the expected conditional standard deviation of the true error.

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

    International Nuclear Information System (INIS)

    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

  8. 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.

  9. AUTO CLAIM FRAUD DETECTION USING MULTI CLASSIFIER SYSTEM

    Directory of Open Access Journals (Sweden)

    Luis Alexandre Rodrigues

    2014-06-01

    Full Text Available Through a cost matrix and a combination of classifiers, this work identifies the most economical model to perform the detection of suspected cases of fraud in a dataset of automobile claims. The experiments performed by this work show that working more deeply in sampled data in the training phase and test phase of each classifier is possible obtain a more economic model than other model presented in the literature.

  10. Automatic Genre Classification of Latin Music Using Ensemble of Classifiers

    OpenAIRE

    Silla Jr, Carlos N.; Kaestner, Celso A.A.; Koerich, Alessandro L.

    2006-01-01

    This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with the aim of improving music genre classification accuracy. Experiments carried out on a dataset conta...

  11. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. PMID

  12. Subtractive Fuzzy Classifier Based Driver Distraction Levels Classification Using EEG

    OpenAIRE

    Wali, Mousa Kadhim; Murugappan, Murugappan; Ahmad, Badlishah

    2013-01-01

    [Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20–35 years, who were free from any disease, participated in this study. Wireless EEG signals were recorded by 14 electrodes during four types of distraction stimuli (Global Position Systems (GPS), ...

  13. Classification of Breast Cancer Using SVM Classifier Technique

    OpenAIRE

    B.Senthil Murugan; S.Srirambabu; Santhosh Kumar. V

    2010-01-01

    This paper proposes a technique for classifying the breast cancer from mammogram. The proposed system aims at developing the visualization tool for detecting the breast cancer and minimizing the scheme of detection. The detection method is organized as follows: (a) Image Enhancement (b) Segmentation (c) Feature extraction (d) Classification using SVM classifier Technique. Image enhancement step concentrates on converting an image to more and better understandable level thereby applying Median...

  14. Classifying pedestrian shopping behaviour according to implied heuristic choice rules

    OpenAIRE

    Shigeyuki Kurose; Aloys W J Borgers; Timmermans, Harry J. P.

    2001-01-01

    Our aim in this paper is to build and test a model which classifies and identifies pedestrian shopping behaviour in a shopping centre by using temporal and spatial choice heuristics. In particular, the temporal local-distance-minimising, total-distance-minimising, and global-distance-minimising heuristic choice rules and spatial nearest-destination-oriented, farthest-destination-oriented, and intermediate-destination-oriented choice rules are combined to classify and identify the stop sequenc...

  15. Classifying Tweet Level Judgements of Rumours in Social Media

    OpenAIRE

    Lukasik, Michal; Cohn, Trevor; Bontcheva, Kalina

    2015-01-01

    Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We formulate the problem of classifying tweet level judgements of rumours as a supervised learning task. Both supervised and unsupervised domain adaptation are considered, in which tweets from a rumour are classified on the basis of other annotated rumours. We demonstrate how multi-task learning helps achieve good results on rumours from t...

  16. MASTER REGULATORS USED AS BREAST CANCER METASTASIS CLASSIFIER*

    OpenAIRE

    Lim, Wei Keat; Lyashenko, Eugenia; Califano, Andrea

    2009-01-01

    Computational identification of prognostic biomarkers capable of withstanding follow-up validation efforts is still an open challenge in cancer research. For instance, several gene expression profiles analysis methods have been developed to identify gene signatures that can classify cancer sub-phenotypes associated with poor prognosis. However, signatures originating from independent studies show only minimal overlap and perform poorly when classifying datasets other than the ones they were g...

  17. Mining housekeeping genes with a Naive Bayes classifier

    OpenAIRE

    Aitken Stuart; De Ferrari Luna

    2006-01-01

    Abstract Background Traditionally, housekeeping and tissue specific genes have been classified using direct assay of mRNA presence across different tissues, but these experiments are costly and the results not easy to compare and reproduce. Results In this work, a Naive Bayes classifier based only on physical and functional characteristics of genes already available in databases, like exon length and measures of chromatin compactness, has achieved a 97% success rate in classification of human...

  18. Mining housekeeping genes with a Naive Bayes classifier

    OpenAIRE

    Ferrari, Luna De; Aitken, Stuart

    2006-01-01

    BACKGROUND: Traditionally, housekeeping and tissue specific genes have been classified using direct assay of mRNA presence across different tissues, but these experiments are costly and the results not easy to compare and reproduce.RESULTS: In this work, a Naive Bayes classifier based only on physical and functional characteristics of genes already available in databases, like exon length and measures of chromatin compactness, has achieved a 97% success rate in classification of human houseke...

  19. Dealing with contaminated datasets: An approach to classifier training

    Science.gov (United States)

    Homenda, Wladyslaw; Jastrzebska, Agnieszka; Rybnik, Mariusz

    2016-06-01

    The paper presents a novel approach to classification reinforced with rejection mechanism. The method is based on a two-tier set of classifiers. First layer classifies elements, second layer separates native elements from foreign ones in each distinguished class. The key novelty presented here is rejection mechanism training scheme according to the philosophy "one-against-all-other-classes". Proposed method was tested in an empirical study of handwritten digits recognition.

  20. The Virtually Cyclic Classifying Space of the Heisenberg Group

    OpenAIRE

    Manion, Andrew; Pham, Lisa; Poelhuis, Jonathan

    2008-01-01

    We are interested in the relationship between the virtual cohomological dimension (or vcd) of a discrete group Gamma and the smallest possible dimension of a model for the classifying space of Gamma relative to its family of virtually cyclic subgroups. In this paper we construct a model for the virtually cyclic classifying space of the Heisenberg group. This model has dimension 3, which equals the vcd of the Heisenberg group. We also prove that there exists no model of dimension less than 3.

  1. 48 CFR 952.223-76 - Conditional payment of fee or profit-safeguarding restricted data and other classified...

    Science.gov (United States)

    2010-10-01

    ... that may warrant a reduction below the specified range (see 48 CFR 904.402(c) and 48 CFR 923.7002(a)(2... performance (including effective resource allocation) and to support DOE corporate decision-making (e.g... other information classified as Top Secret, any classification level of information in a Special...

  2. 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.

  3. Locating and classifying defects using an hybrid data base

    Energy Technology Data Exchange (ETDEWEB)

    Luna-Aviles, A; Diaz Pineda, A [Tecnologico de Estudios Superiores de Coacalco. Av. 16 de Septiembre 54, Col. Cabecera Municipal. C.P. 55700 (Mexico); Hernandez-Gomez, L H; Urriolagoitia-Calderon, G; Urriolagoitia-Sosa, G [Instituto Politecnico Nacional. ESIME-SEPI. Unidad Profesional ' Adolfo Lopez Mateos' Edificio 5, 30 Piso, Colonia Lindavista. Gustavo A. Madero. 07738 Mexico D.F. (Mexico); Durodola, J F [School of Technology, Oxford Brookes University, Headington Campus, Gipsy Lane, Oxford OX3 0BP (United Kingdom); Beltran Fernandez, J A, E-mail: alelunaav@hotmail.com, E-mail: luishector56@hotmail.com, E-mail: jdurodola@brookes.ac.uk

    2011-07-19

    A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200x20x5 mm. One half of them were plain and the other half has a notch (3 mm x 4 mm) which is close to the defect area (19 mm x 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

  4. Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

    OpenAIRE

    Yan, Xu; Mou, Lili; Li, Ge; Chen, Yunchuan; Peng, Hao; Jin, Zhi

    2015-01-01

    Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) ...

  5. Intellect Sensing of Neural Network that Trained to Classify Complex Signals

    OpenAIRE

    Reznik, A.; Galinskaya, A.

    2003-01-01

    An experimental comparison of information features used by neural network is performed. The sensing method was used. Suboptimal classifier agreeable to the gaussian model of the training data was used as a probe. Neural nets with architectures of perceptron and feedforward net with one hidden layer were used. The experiments were carried out with spatial ultrasonic data, which are used for car’s passenger safety system neural controller learning. In this paper we show that a n...

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

    Science.gov (United States)

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    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. PMID:27320688

  7. Classifier models and architectures for EEG-based neonatal seizure detection

    International Nuclear Information System (INIS)

    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

  8. Dynamic weighted voting for multiple classifier fusion: a generalized rough set method

    Institute of Scientific and Technical Information of China (English)

    Sun Liang; Han Chongzhao

    2006-01-01

    To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting (KD-DWV) is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables (DTs) are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory (DST), from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting (PV).

  9. 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.

  10. Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers

    Science.gov (United States)

    Tiwari, Laxmi Kant; Sinha, Satish K.; Saran, Sameer; Tolpekin, Valentyn A.; Raju, Penumetcha L. N.

    2016-01-01

    Maximum likelihood classifier (MLC) and support vector machines (SVMs) are commonly used supervised classification methods in remote sensing applications. MLC is a parametric method, whereas SVM is a nonparametric method. In an environmental application, a hybrid scheme is designed to identify forest encroachment (FE) pockets by classifying medium-resolution remote sensing images with SVM, incorporating knowledge-base and GPS readings in the geographical information system. The classification scheme has enabled us to identify small scattered noncontiguous FE pockets supported by ground truthing. On Baratang Island, the detected FE area from the classified thematic map for the year 2003 was ˜202 ha, and for the year 2013, the encroachment was ˜206 ha. While some of the older FE pockets were vacated, new FE pockets appeared in the area. Furthermore, comparisons of different classification results in terms of Z-statistics indicate that linear SVM is superior to MLC, whereas linear and nonlinear SVM are not significantly different. Accuracy assessment shows that SVM-based classification results have higher accuracy than MLC-based results. Statistical accuracy in terms of kappa values achieved for the linear SVM-classified thematic maps for the years 2003 and 2013 is 0.98 and 1.0, respectively.

  11. A GIS semiautomatic tool for classifying and mapping wetland soils

    Science.gov (United States)

    Moreno-Ramón, Héctor; Marqués-Mateu, Angel; Ibáñez-Asensio, Sara

    2016-04-01

    generated a set of layers with the geographical information, which corresponded with each diagnostic criteria. Finally, the superposition of layers generated the different homogeneous soil units where the soil scientist should locate the soil profiles. Historically, the Albufera of Valencia has been classified as a soil homogeneous unit, but it was demonstrated that there were six homogeneous units after the methodology and the GIS tool application. In that regard, the outcome reveals that it had been necessary to open only six profiles, against the 19 profiles opened when the real study was carried out. As a conclusion, the methodology and the SIG tool demonstrated that could be employed in areas where the soil forming-factors cannot be distinguished. The application of rapid measurement methods and this methodology could economise the definition process of homogeneous units.

  12. [Horticultural plant diseases multispectral classification using combined classified methods].

    Science.gov (United States)

    Feng, Jie; Li, Hong-Ning; Yang, Wei-Ping; Hou, De-Dong; Liao, Ning-Fang

    2010-02-01

    The research on multispectral data disposal is getting more and more attention with the development of multispectral technique, capturing data ability and application of multispectral technique in agriculture practice. In the present paper, a cultivated plant cucumber' familiar disease (Trichothecium roseum, Sphaerotheca fuliginea, Cladosporium cucumerinum, Corynespora cassiicola, Pseudoperonospora cubensis) is the research objects. The cucumber leaves multispectral images of 14 visible light channels, near infrared channel and panchromatic channel were captured using narrow-band multispectral imaging system under standard observation and illumination environment, and 210 multispectral data samples which are the 16 bands spectral reflectance of different cucumber disease were obtained. The 210 samples were classified by distance, relativity and BP neural network to discuss effective combination of classified methods for making a diagnosis. The result shows that the classified effective combination of distance and BP neural network classified methods has superior performance than each method, and the advantage of each method is fully used. And the flow of recognizing horticultural plant diseases using combined classified methods is presented. PMID:20384138

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

    Science.gov (United States)

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

    2014-05-01

    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.

  14. 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.

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

    International Nuclear Information System (INIS)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  17. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports.

    Science.gov (United States)

    López Pineda, Arturo; Ye, Ye; Visweswaran, Shyam; Cooper, Gregory F; Wagner, Michael M; Tsui, Fuchiang Rich

    2015-12-01

    Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) free-text reports can be of value to improve influenza detection in real time. This paper studies seven machine learning (ML) classifiers for influenza detection, compares their diagnostic capabilities against an expert-built influenza Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We identified 31,268 ED reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract influenza-related findings and to encode them into one of three values: Acute, Non-acute, and Missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates ED reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of infectious diseases. PMID:26385375

  18. 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

    We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types...... as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups......: (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...

  19. 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.

  20. 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.

  1. Multiple-instance learning as a classifier combining problem

    DEFF Research Database (Denmark)

    Li, Yan; Tax, David M.J.; Duin, Robert P.W.;

    2013-01-01

    In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the...... assumption that instances are drawn from a mixture distribution of the concept and the non-concept, which leads to a convenient way to solve MIL as a classifier combining problem. It is shown that instances can be classified with any standard supervised classifier by re-weighting the classification....... The method is tested on a toy data set and various benchmark data sets, and shown to provide results comparable to state-of-the-art MIL methods. (C) 2012 Elsevier Ltd. All rights reserved....

  2. WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar(DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.

  3. Iris Recognition Based on LBP and Combined LVQ Classifier

    CERN Document Server

    Shams, M Y; Nomir, O; El-Awady, R M; 10.5121/ijcsit.2011.3506

    2011-01-01

    Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction, and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris da...

  4. 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.

  5. What Does(n't) K-theory Classify?

    CERN Document Server

    Evslin, J

    2006-01-01

    We review various K-theory classification conjectures in string theory. Sen conjecture based proposals classify D-brane trajectories in backgrounds with no H flux, while Freed-Witten anomaly based proposals classify conserved RR charges and magnetic RR fluxes in topologically time-independent backgrounds. In exactly solvable CFTs a classification of well-defined boundary states implies that there are branes representing every twisted K-theory class. Some of these proposals fail to respect the self-duality of the RR fields in the democratic formulation of type II supergravity and none respect S-duality in type IIB string theory. We discuss two applications. The twisted K-theory classification has led to a conjecture for the topology of the T-dual of any configuration. In the Klebanov-Strassler geometry twisted K-theory classifies universality classes of baryonic vacua.

  6. Information barriers

    International Nuclear Information System (INIS)

    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 term is more recent. In January 1999, an Information Barrier Working Group (IBWG) was formed in the United States to help coordinate technical efforts related to information barrier research and development (R and D). This paper presents an overview of the efforts of this group, by its present and former 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. IBWG perspective, the 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 its 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 in the United States that in the absence of any agreement to share classified nuclear weapons design information while implementing an inspection regime, the need to protect host country classified warhead design information is paramount and overrules the need to provide confidence to the inspecting party regarding the accuracy and reproducibility of the measurements. The U.S. IBWG has reached a consensus on several critical design elements that define a general standard for radiation signature information barrier design. Technical specialists from cooperating parties must be

  7. Text Classification: Classifying Plain Source Files with Neural Network

    Directory of Open Access Journals (Sweden)

    Jaromir Veber

    2010-10-01

    Full Text Available The automated text file categorization has an important place in computer engineering, particularly in the process called data management automation. A lot has been written about text classification and the methods allowing classification of these files are well known. Unfortunately most studies are theoretical and for practical implementation more research is needed. I decided to contribute with a research focused on creating of a classifier for different kinds of programs (source files, scripts…. This paper will describe practical implementation of the classifier for text files depending on file content.

  8. Dynamic Classifier Systems and their Applications to Random Forest Ensembles

    Czech Academy of Sciences Publication Activity Database

    Štefka, David; Holeňa, Martin

    Berlin: Springer, 2009 - (Kolehmainen, M.; Toivanen, P.; Beliczynski, B.), s. 458-468. (Lecture Notes in Computer Science. 5495). ISBN 978-3-642-04920-0. [ICANNGA'2009. International conference /9./. Kuopio (FI), 23.04.2009-25.04.2009] R&D Projects: GA AV ČR 1ET100300517; GA ČR GA201/08/0802 Institutional research plan: CEZ:AV0Z10300504 Keywords : classifier combining * dynamic classifier aggregation * random forests * classification Subject RIV: IN - Informatics, Computer Science

  9. A Combination of Off-line and On-line Learning to Classifier Grids for Object Detection

    OpenAIRE

    Nguyen Dang Binh

    2016-01-01

    We propose a new method for object detection by combining off-line and on-line boosting learning to classifier grids based on visual information without human intervention concerned to intelligent surveillance system. It allows for combine information labeled and unlabeled use different contexts to update the system, which is not available at off-line training time. The main goal is to develop an adaptive but robust system and to combine prior knowledge with new information in an unsupervised...

  10. Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover

    Directory of Open Access Journals (Sweden)

    Cheng-Kai Wang

    2014-01-01

    Full Text Available This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR and middle-infrared (MIR lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation when classifying land cover.

  11. The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity.

    Science.gov (United States)

    Manning, Timmy; Walsh, Paul

    2016-04-01

    This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear classifier using only amino acid sequence and identity information. Ensembles of these nonlinear classifiers using the physicochemical properties of the substrate are demonstrated to consistently outperform the recently published state-of-the-art linear support vector machine based approach in out-of-sample evaluations. PMID:27212259

  12. 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

  13. Support vector machines classifiers of physical activities in preschoolers

    Science.gov (United States)

    The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

  14. Packet Payload Inspection Classifier in the Network Flow Level

    Directory of Open Access Journals (Sweden)

    N.Kannaiya Raja

    2012-06-01

    Full Text Available The network have in the world highly congested channels and topology which was dynamicallycreated with high risk. In this we need flow classifier to find the packet movement in the network.In this paper we have to be developed and evaluated TCP/UDP/FTP/ICMP based on payloadinformation and port numbers and number of flags in the packet for highly flow of packets in thenetwork. The primary motivations of this paper all the valuable protocols are used legally toprocess find out the end user by using payload packet inspection, and also used evaluationshypothesis testing approach. The effective use of tamper resistant flow classifier has used in onenetwork contexts domain and developed in a different Berkeley and Cambridge, the classificationand accuracy was easily found through the packet inspection by using different flags in thepackets. While supervised classifier training specific to the new domain results in much betterclassification accuracy, we also formed a new approach to determine malicious packet and find apacket flow classifier and send correct packet to destination address.

  15. 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.

  16. Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

    Science.gov (United States)

    Assaleh, Khaled; Al-Rousan, M.

    2005-12-01

    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.

  17. Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures

    Czech Academy of Sciences Publication Activity Database

    Štefka, D.; Holeňa, Martin

    2015-01-01

    Roč. 270, 1 July (2015), s. 25-52. ISSN 0165-0114 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : Fuzzy integral * Fuzzy measure * Dynamic classifier aggregation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.986, year: 2014

  18. 18 CFR 367.18 - Criteria for classifying leases.

    Science.gov (United States)

    2010-04-01

    ... classification of the lease under the criteria in paragraph (a) of this section had the changed terms been in... the lessee) must not give rise to a new classification of a lease for accounting purposes. ... ACT General Instructions § 367.18 Criteria for classifying leases. (a) If, at its inception, a...

  19. Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

    Directory of Open Access Journals (Sweden)

    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.

  20. 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...

  1. Classifying helicopter gearbox faults using a normalized energy metric

    Science.gov (United States)

    Samuel, Paul D.; Pines, Darryll J.

    2001-02-01

    A normalized energy metric is used to classify seeded faults of the OH-58A main transmission. This gearbox comprises a two-stage transmission with an overall reduction of 17.44:1. Loaded gearbox test runs are used to evaluate the sensitivity of a non-stationary fault metric for early fault detection and classification. The non-stationary fault metric consists of a simple normalized energy index developed to account for a redistribution of sideband energy of the dominant mesh frequency and its harmonics in the presence of actual gearbox faults. This index is used to qualitatively assess the presence, type and location of gearbox faults. In this work, elements of the normalized energy metric are assembled into a feature vector to serve as input into a self-organizing Kohonen neural network classifier. This classifier maps vibration features onto a two-dimensional grid. A feedforward back propagation neural network is then used to classify different faults according to how they cluster on the two-dimensional self-organizing map. Gearbox faults of OH-58A main transmission considered in this study include sun gear spalling and spiral bevel gear scoring. Results from the classification suggest that the normalized energy metric is reasonably robust against false alarms for certain geartrain faults.

  2. Subtractive fuzzy classifier based driver distraction levels classification using EEG.

    Science.gov (United States)

    Wali, Mousa Kadhim; Murugappan, Murugappan; Ahmad, Badlishah

    2013-09-01

    [Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20-35 years, who were free from any disease, participated in this study. Wireless EEG signals were recorded by 14 electrodes during four types of distraction stimuli (Global Position Systems (GPS), music player, short message service (SMS), and mental tasks). We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG. Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5). Mean ± SD was calculated and analysis of variance (ANOVA) was performed. A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction. PMID:24259914

  3. Scoring and Classifying Examinees Using Measurement Decision Theory

    Science.gov (United States)

    Rudner, Lawrence M.

    2009-01-01

    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…

  4. 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. 

  5. Group-cohomology refinement to classify G-symplectic manifolds

    International Nuclear Information System (INIS)

    'Pseudo-cohomology', as a refinement of Lie group cohomology, is soundly studied aiming at classifying the symplectic manifolds associated with Lie groups. In this study, the framework of symplectic cohomology provides fundamental new insight, which enriches the analysis previously developed in the setting of Cartan-Eilenberg H2(G,U(1)) cohomology

  6. Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images

    International Nuclear Information System (INIS)

    An automated computer-aided diagnosis system is developed to classify benign and malignant thyroid nodules using multi-stained fine needle aspiration biopsy (FNAB) cytological images. In the first phase, the image segmentation is performed to remove the background staining information and retain the appropriate foreground cell objects in cytological images using mathematical morphology and watershed transform segmentation methods. Subsequently, statistical features are extracted using two-level discrete wavelet transform (DWT) decomposition, gray level co-occurrence matrix (GLCM) and Gabor filter based methods. The classifiers k-nearest neighbor (k-NN), Elman neural network (ENN) and support vector machine (SVM) are tested for classifying benign and malignant thyroid nodules. The combination of watershed segmentation, GLCM features and k-NN classifier results a lowest diagnostic accuracy of 60 %. The highest diagnostic accuracy of 93.33 % is achieved by ENN classifier trained with the statistical features extracted by Gabor filter bank from the images segmented by morphology and watershed transform segmentation methods. It is also observed that SVM classifier results its highest diagnostic accuracy of 90 % for DWT and Gabor filter based features along with morphology and watershed transform segmentation methods. The experimental results suggest that the developed system with multi-stained thyroid FNAB images would be useful for identifying thyroid cancer irrespective of staining protocol used.

  7. Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia.

    Directory of Open Access Journals (Sweden)

    Alessandra Cesano

    Full Text Available Single-cell network profiling (SCNP data generated from multi-parametric flow cytometry analysis of bone marrow (BM and peripheral blood (PB samples collected from patients >55 years old with non-M3 AML were used to train and validate a diagnostic classifier (DXSCNP for predicting response to standard induction chemotherapy (complete response [CR] or CR with incomplete hematologic recovery [CRi] versus resistant disease [RD]. SCNP-evaluable patients from four SWOG AML trials were randomized between Training (N = 74 patients with CR, CRi or RD; BM set = 43; PB set = 57 and Validation Analysis Sets (N = 71; BM set = 42, PB set = 53. Cell survival, differentiation, and apoptosis pathway signaling were used as potential inputs for DXSCNP. Five DXSCNP classifiers were developed on the SWOG Training set and tested for prediction accuracy in an independent BM verification sample set (N = 24 from ECOG AML trials to select the final classifier, which was a significant predictor of CR/CRi (area under the receiver operating characteristic curve AUROC = 0.76, p = 0.01. The selected classifier was then validated in the SWOG BM Validation Set (AUROC = 0.72, p = 0.02. Importantly, a classifier developed using only clinical and molecular inputs from the same sample set (DXCLINICAL2 lacked prediction accuracy: AUROC = 0.61 (p = 0.18 in the BM Verification Set and 0.53 (p = 0.38 in the BM Validation Set. Notably, the DXSCNP classifier was still significant in predicting response in the BM Validation Analysis Set after controlling for DXCLINICAL2 (p = 0.03, showing that DXSCNP provides information that is independent from that provided by currently used prognostic markers. Taken together, these data show that the proteomic classifier may provide prognostic information relevant to treatment planning beyond genetic mutations and traditional prognostic factors in elderly AML.

  8. Immigrants' health in Europe: A cross-classified multilevel approach to examine origin country, destination country, and community effects

    NARCIS (Netherlands)

    Huijts, T.H.M.; Kraaykamp, G.L.M.

    2012-01-01

    In this study, we examined origin, destination, and community effects on first- and second-generation immigrants health in Europe. We used information from the European Social Surveys (20022008) on 19,210 immigrants from 123 countries of origin, living in 31 European countries. Cross-classified mult

  9. Inferring Functional Brain States Using Temporal Evolution of Regularized Classifiers

    Directory of Open Access Journals (Sweden)

    Andrey Zhdanov

    2007-08-01

    Full Text Available We present a framework for inferring functional brain state from electrophysiological (MEG or EEG brain signals. Our approach is adapted to the needs of functional brain imaging rather than EEG-based brain-computer interface (BCI. This choice leads to a different set of requirements, in particular to the demand for more robust inference methods and more sophisticated model validation techniques. We approach the problem from a machine learning perspective, by constructing a classifier from a set of labeled signal examples. We propose a framework that focuses on temporal evolution of regularized classifiers, with cross-validation for optimal regularization parameter at each time frame. We demonstrate the inference obtained by this method on MEG data recorded from 10 subjects in a simple visual classification experiment, and provide comparison to the classical nonregularized approach.

  10. 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.

  11. The fuzzy gene filter: A classifier performance assesment

    CERN Document Server

    Perez, Meir

    2011-01-01

    The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.

  12. The Emotion Sign: Human Motion Analysis Classifying Specific Emotion

    Directory of Open Access Journals (Sweden)

    Yuichi Kobayashi

    2008-09-01

    Full Text Available We examine the relationship between human motion and emotions. With recent improvement of sensing technologies, although precise human motion can be measured, the amount of data grows enormously. In this paper, we propose a new analysis method which can describe large amount of data rationally. This method can be used to classify human motions associated with specific emotions. Our approach to motion data analysis is to apply higher order singular value decomposition (HOSVD directly to motion data. HOSVD can generate a compact vector which specifies each emotion common among people. Experimentally, we obtained motion capture data for “gait” and “standing” actions related to six basic emotions. Human gait motion was also created with an animator. For these motion data, our analysis showed that our method can classify the human motions specific to each emotion.

  13. Characterizing and classifying uranium yellow cakes: A background

    Science.gov (United States)

    Hausen, D. M.

    1998-12-01

    Uranium concentrates obtained from leach solutions, known as uranium yellow cakes, represent an intermediate step in the processing of uranium ores. Yellow cake concentrates are prepared by various metallurgical methods, depending on the types of ores. Samples of yellow cakes prepared under various methods were analyzed; examined in detail by means of x-ray diffraction, infrared spectra, and wet chemical methods; and classified by mineralogic methods. The cakes were classified as uranyl hydroxide hydrate, basic uranyl sulfate hydrate, sodium para-uranate, and uranyl peroxide hydrate. The experimental preparation methods and characterization methodology used are described, and the significance of structural types to the physical and chemical properties of yellow cake production, as well as the pyrolytic transformations at high temperatures, are discussed.

  14. Feasibility study for banking loan using association rule mining classifier

    Directory of Open Access Journals (Sweden)

    Agus Sasmito Aribowo

    2015-03-01

    Full Text Available The problem of bad loans in the koperasi can be reduced if the koperasi can detect whether member can complete the mortgage debt or decline. The method used for identify characteristic patterns of prospective lenders in this study, called Association Rule Mining Classifier. Pattern of credit member will be converted into knowledge and used to classify other creditors. Classification process would separate creditors into two groups: good credit and bad credit groups. Research using prototyping for implementing the design into an application using programming language and development tool. The process of association rule mining using Weighted Itemset Tidset (WIT–tree methods. The results shown that the method can predict the prospective customer credit. Training data set using 120 customers who already know their credit history. Data test used 61 customers who apply for credit. The results concluded that 42 customers will be paying off their loans and 19 clients are decline

  15. Efficient iris recognition via ICA feature and SVM classifier

    Institute of Scientific and Technical Information of China (English)

    Wang Yong; Xu Luping

    2007-01-01

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

  16. Predicting Cutting Forces in Aluminum Using Polynomial Classifiers

    Science.gov (United States)

    Kadi, H. El; Deiab, I. M.; Khattab, A. A.

    Due to increased calls for environmentally benign machining processes, there has been focus and interest in making processes more lean and agile to enhance efficiency, reduce emissions and increase profitability. One approach to achieving lean machining is to develop a virtual simulation environment that enables fast and reasonably accurate predictions of various machining scenarios. Polynomial Classifiers (PCs) are employed to develop a smart data base that can provide fast prediction of cutting forces resulting from various combinations of cutting parameters. With time, the force model can expand to include different materials, tools, fixtures and machines and would be consulted prior to starting any job. In this work, first, second and third order classifiers are used to predict the cutting coefficients that can be used to determine the cutting forces. Predictions obtained using PCs are compared to experimental results and are shown to be in good agreement.

  17. Nonlinear interpolation fractal classifier for multiple cardiac arrhythmias recognition

    Energy Technology Data Exchange (ETDEWEB)

    Lin, C.-H. [Department of Electrical Engineering, Kao-Yuan University, No. 1821, Jhongshan Rd., Lujhu Township, Kaohsiung County 821, Taiwan (China); Institute of Biomedical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan (China)], E-mail: eechl53@cc.kyu.edu.tw; Du, Y.-C.; Chen Tainsong [Institute of Biomedical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan (China)

    2009-11-30

    This paper proposes a method for cardiac arrhythmias recognition using the nonlinear interpolation fractal classifier. A typical electrocardiogram (ECG) consists of P-wave, QRS-complexes, and T-wave. Iterated function system (IFS) uses the nonlinear interpolation in the map and uses similarity maps to construct various data sequences including the fractal patterns of supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Grey relational analysis (GRA) is proposed to recognize normal heartbeat and cardiac arrhythmias. The nonlinear interpolation terms produce family functions with fractal dimension (FD), the so-called nonlinear interpolation function (NIF), and make fractal patterns more distinguishing between normal and ill subjects. The proposed QRS classifier is tested using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Compared with other methods, the proposed hybrid methods demonstrate greater efficiency and higher accuracy in recognizing ECG signals.

  18. 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

  19. Classifying paragraph types using linguistic features: Is paragraph positioning important?

    OpenAIRE

    Scott A. Crossley, Kyle Dempsey & Danielle S. McNamara

    2011-01-01

    This study examines the potential for computational tools and human raters to classify paragraphs based on positioning. In this study, a corpus of 182 paragraphs was collected from student, argumentative essays. The paragraphs selected were initial, middle, and final paragraphs and their positioning related to introductory, body, and concluding paragraphs. The paragraphs were analyzed by the computational tool Coh-Metrix on a variety of linguistic features with correlates to textual cohesion ...

  20. Classifying Floating Potential Measurement Unit Data Products as Science Data

    Science.gov (United States)

    Coffey, Victoria; Minow, Joseph

    2015-01-01

    We are Co-Investigators for the Floating Potential Measurement Unit (FPMU) on the International Space Station (ISS) and members of the FPMU operations and data analysis team. We are providing this memo for the purpose of classifying raw and processed FPMU data products and ancillary data as NASA science data with unrestricted, public availability in order to best support science uses of the data.

  1. Image Replica Detection based on Binary Support Vector Classifier

    OpenAIRE

    Maret, Y.; Dufaux, F.; Ebrahimi, T.

    2005-01-01

    In this paper, we present a system for image replica detection. More specifically, the technique is based on the extraction of 162 features corresponding to texture, color and gray-level characteristics. These features are then weighted and statistically normalized. To improve training and performances, the features space dimensionality is reduced. Lastly, a decision function is generated to classify the test image as replica or non-replica of a given reference image. Experimental results sho...

  2. Controlled self-organisation using learning classifier systems

    OpenAIRE

    Richter, Urban Maximilian

    2009-01-01

    The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed.

  3. Learning Rates for ${l}^{1}$ -Regularized Kernel Classifiers

    OpenAIRE

    Hongzhi Tong; Di-Rong Chen; Fenghong Yang

    2013-01-01

    We consider a family of classification algorithms generated from a regularization kernel scheme associated with ${l}^{1}$ -regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. The error decomposition includes approximation error, hypothesis error, and sample error. We apply some novel techniques to estimate the hypothesis error and sample error. Learning rates are eventually derive...

  4. Higher operations in string topology of classifying spaces

    OpenAIRE

    Lahtinen, Anssi

    2015-01-01

    Examples of non-trivial higher string topology operations have been regrettably rare in the literature. In this paper, working in the context of string topology of classifying spaces, we provide explicit calculations of a wealth of non-trivial higher string topology operations associated to a number of different Lie groups. As an application of these calculations, we obtain an abundance of interesting homology classes in the twisted homology groups of automorphism groups of free groups, the o...

  5. Molecular Characteristics in MRI-Classified Group 1 Glioblastoma Multiforme

    OpenAIRE

    Chin-HsingAnnieLin; RebeccaAIhrie; ArturoAlvarez-Buylla; RobertNEisenman

    2013-01-01

    Glioblastoma multiforme (GBM) is a clinically and pathologically heterogeneous brain tumor. Previous studies of transcriptional profiling have revealed biologically relevant GBM subtypes associated with specific mutations and dysregulated pathways. Here, we applied a modified proteome to uncover abnormal protein expression profile in a MRI-classified group I GBM (GBM1), which has a spatial relationship with one of the adult neural stem cell niches, subventricular zone (SVZ). Most importantly,...

  6. Classifying racist texts using a support vector machine

    OpenAIRE

    Greevy, Edel; Alan F. SMEATON

    2004-01-01

    In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system to automatically classify racist texts. Detecting racism is difficult because the presence of indicator words is insufficient to indicate racist texts, unlike some other text classification tasks. Support Vector Machines (SVM) are used to automatically categorise web pages based on whether or not they are racist. Different interpretations of what constitutes a term are taken, a...

  7. VIRTUAL MINING MODEL FOR CLASSIFYING TEXT USING UNSUPERVISED LEARNING

    OpenAIRE

    S. Koteeswaran; E. Kannan; P. Visu

    2014-01-01

    In real world data mining is emerging in various era, one of its most outstanding performance is held in various research such as Big data, multimedia mining, text mining etc. Each of the researcher proves their contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are re...

  8. An alternative educational indicator for classifying Secondary Schools in Portugal

    OpenAIRE

    Gonçalves, A. Manuela; Costa, Marco; De Oliveira, Mário,

    2015-01-01

    The purpose of this paper aims at carrying out a study in the area of Statistics for classifying Portuguese Secondary Schools (both mainland and islands: “Azores” and “Madeira”),taking into account the results achievedby their students in both national examinations and internal assessment. The main according consists of identifying groups of schools with different performance levels by considering the sub-national public and private education systems’ as well as their respective geographic lo...

  9. Applying deep learning to classify pornographic images and videos

    OpenAIRE

    Moustafa, Mohamed

    2015-01-01

    It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a clas...

  10. A Mobile Service Delivery Platform forWeb Classifieds

    OpenAIRE

    Mahmood, Azam

    2013-01-01

    The Mobidoo Mobile Service Delivery Platform (MSDP) provides opportunity to the service providers to add online services by creating classifieds and advertising them to end users. These services can either be provided free of cost or with cost. Users can facilitate from these services by showing their interest and can get that particular service from service provider via ADMIN authentication or can also just surf through the services available on mobile web application. Main users of the appl...

  11. Application of dispersion analysis for determining classifying separation size

    OpenAIRE

    Golomeova, Mirjana; Golomeov, Blagoj; Krstev, Boris; Zendelska, Afrodita; Krstev, Aleksandar

    2009-01-01

    The paper presents the procedure of mathematical modelling the cut point of copper ore classifying by laboratory hydrocyclone. The application of dispersion analysis and planning with Latin square makes possible significant reduction the number of tests. Tests were carried out by D-100 mm hydrocyclone. Variable parameters are as follows: content of solid in pulp, underflow diameter, overflow diameter and inlet pressure. The cut point is determined by partition curve. The obtained mathemat...

  12. Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder

    OpenAIRE

    Zhang, Xi; Fu, Yanwei; Zang, Andi; Sigal, Leonid; Agam, Gady

    2015-01-01

    We propose a method for using synthetic data to help learning classifiers. Synthetic data, even is generated based on real data, normally results in a shift from the distribution of real data in feature space. To bridge the gap between the real and synthetic data, and jointly learn from synthetic and real data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by suing MCAE, it is possible to learn a better feature representation for classification. To evaluate the proposed a...

  13. Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks

    OpenAIRE

    Cho, Kyunghyun; Chen, Xi

    2013-01-01

    The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative...

  14. Building Road-Sign Classifiers Using a Trainable Similarity Measure

    Czech Academy of Sciences Publication Activity Database

    Paclík, P.; Novovičová, Jana; Duin, R.P.W.

    2006-01-01

    Roč. 7, č. 3 (2006), s. 309-321. ISSN 1524-9050 R&D Projects: GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : classifier system design * road-sign classification * similarity data representation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.434, year: 2006 http://www.ewh.ieee.org/tc/its/trans.html

  15. Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

    OpenAIRE

    Urbanowicz, Ryan J; Jason H Moore

    2009-01-01

    If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing n...

  16. Switching Fuzzy Classifier for Classification of EEG Spectrograms

    Czech Academy of Sciences Publication Activity Database

    Coufal, David

    Budapest: Budapest Tech, 2008, s. 143-150. ISBN 978-963-7154-82-9. [CINTI 2008. International Symposium of Hungarian Researchers on Computational Intelligence and Informatics /9./. Budapest (HU), 06.11.2008-08.11.2008] R&D Projects: GA MDS 1F84B/042/520 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy classifier * classification tree * EEG spectrograms Subject RIV: AQ - Safety, Health Protection, Human - Machine

  17. The three-dimensional origin of the classifying algebra

    OpenAIRE

    Fuchs, Jurgen; Schweigert, Christoph; Stigner, Carl

    2009-01-01

    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 str...

  18. 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.

  19. Evolutionary optimization of classifiers and features for single-trial EEG Discrimination

    Directory of Open Access Journals (Sweden)

    Wessberg Johan

    2007-08-01

    Full Text Available Abstract Background State-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces. This paper investigates one such novel approach, evaluating how individual classifier and feature subset tailoring affects classification of single-trial EEG finger movements. The discrete wavelet transform was used to extract signal features that were classified using linear regression and non-linear neural network models, which were trained and architecturally optimized with evolutionary algorithms. The input feature subsets were also allowed to evolve, thus performing feature selection in a wrapper fashion. Filter approaches were implemented as well by limiting the degree of optimization. Results Using only 10 features and 100 patterns, the non-linear wrapper approach achieved the highest validation classification accuracy (subject mean 75%, closely followed by the linear wrapper method (73.5%. The optimal features differed much between subjects, yet some physiologically plausible patterns were observed. Conclusion High degrees of classifier parameter, structure and feature subset tailoring on individual levels substantially increase single-trial EEG classification rates, an important consideration in areas where highly accurate detection rates are essential. Also, the presented method provides insight into the spatial characteristics of finger movement EEG patterns.

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

    International Nuclear Information System (INIS)

    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)

  1. IRIS RECOGNITION BASED ON LBP AND COMBINED LVQ CLASSIFIER

    Directory of Open Access Journals (Sweden)

    M. Z. Rashad

    2011-11-01

    Full Text Available Iris recognition is considered as one of the best biometric methods used for human identification andverification, this is because of its unique features that differ from one person to another, and itsimportance in the security field. This paper proposes an algorithm for iris recognition and classificationusing a system based on Local Binary Pattern and histogram properties as a statistical approaches forfeature extraction , and Combined Learning Vector Quantization Classifier as Neural Network approachfor classification, in order to build a hybrid model depends on both features. The localization andsegmentation techniques are presented using both Canny edge detection and Hough Circular Transformin order to isolate an iris from the whole eye image and for noise detection .Feature vectors results fromLBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptableperformance, and the result is based on majority voting among several LVQ classifier. Different irisdatasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP isworking on a grayscale level so colored iris images should be transformed into a grayscale level. Theproposed system gives a high recognition rate 99.87 % on different iris datasets compared with othermethods.

  2. Patients on weaning trials classified with support vector machines

    International Nuclear Information System (INIS)

    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

  3. 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.

  4. Application of the Naive Bayesian Classifier to optimize treatment decisions

    International Nuclear Information System (INIS)

    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

  5. College students classified with ADHD and the foreign language requirement.

    Science.gov (United States)

    Sparks, Richard L; Javorsky, James; Philips, Lois

    2004-01-01

    The conventional assumption of most disability service providers is that students classified as having attention-deficit/hyperactivity disorder (ADHD) will experience difficulties in foreign language (FL) courses. However, the evidence in support of this assumption is anecdotal. In this empirical investigation, the demographic profiles, overall academic performance, college entrance scores, and FL classroom performance of 68 college students classified as having ADHD were examined. All students had graduated from the same university over a 5-year period. The findings showed that all 68 students had completed the university's FL requirement by passing FL courses. The students' college entrance scores were similar to the middle 50% of freshmen at this university, and their graduating grade point average was similar to the typical graduating senior at the university. The students had participated in both lower (100) and upper (200, 300, 400) level FL courses and had achieved mostly average and above-average grades (A, B, C) in these courses. One student had majored and eight students had minored in an FL. Two thirds of the students passed all of their FL courses without the use of instructional accommodations. In this study, the classification of ADHD did not appear to interfere with participants' performance in FL courses. The findings suggest that students classified as having ADHD should enroll in and fulfill the FL requirement by passing FL courses. PMID:15493238

  6. Exploiting Language Models to Classify Events from Twitter.

    Science.gov (United States)

    Vo, Duc-Thuan; Hai, Vo Thuan; Ock, Cheol-Young

    2015-01-01

    Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events. PMID:26451139

  7. Exploiting Language Models to Classify Events from Twitter

    Directory of Open Access Journals (Sweden)

    Duc-Thuan Vo

    2015-01-01

    Full Text Available Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP, which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets’ features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events.

  8. 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.

  9. ASYMBOOST-BASED FISHER LINEAR CLASSIFIER FOR FACE RECOGNITION

    Institute of Scientific and Technical Information of China (English)

    Wang Xianji; Ye Xueyi; Li Bin; Li Xin; Zhuang Zhenquan

    2008-01-01

    When using AdaBoost to select discriminant features from some feature space (e.g. Gabor feature space) for face recognition, cascade structure is usually adopted to leverage the asymmetry in the distribution of positive and negative samples. Each node in the cascade structure is a classifier trained by AdaBoost with an asymmetric learning goal of high recognition rate but only moderate low false positive rate. One limitation of AdaBoost arises in the context of skewed example distribution and cascade classifiers: AdaBoost minimizes the classification error, which is not guaranteed to achieve the asymmetric node learning goal. In this paper, we propose to use the asymmetric AdaBoost (Asym-Boost) as a mechanism to address the asymmetric node learning goal. Moreover, the two parts of the selecting features and forming ensemble classifiers are decoupled, both of which occur simultaneously in AsymBoost and AdaBoost. Fisher Linear Discriminant Analysis (FLDA) is used on the selected features to learn a linear discriminant function that maximizes the separability of data among the different classes, which we think can improve the recognition performance. The proposed algorithm is dem onstrated with face recognition using a Gabor based representation on the FERET database. Experimental results show that the proposed algorithm yields better recognition performance than AdaBoost itself.

  10. Early Detection of Breast Cancer using SVM Classifier Technique

    Directory of Open Access Journals (Sweden)

    Y.Ireaneus Anna Rejani

    2009-11-01

    Full Text Available This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of (a mammogram enhancement. (b The segmentation of the tumor area. (c The extraction of features from the segmented tumor area. (d The use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include, which classifies the regions using the SVM classifier. The method was tested on 75 mammographic images, from the mini-MIAS database. The methodology achieved a sensitivity of 88.75%.

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

    International Nuclear Information System (INIS)

    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

  12. Use RAPD Analysis to Classify Tea Trees in Yunnan

    Institute of Scientific and Technical Information of China (English)

    SHAO Wan-fang; PANG Rui-hua; DUAN Hong-xing; WANG Ping-sheng; XU Mei; ZHANG Ya-ping; LI Jia-hua

    2003-01-01

    RAPD assessment on genetic variations of 45 tea trees in Yunnan was carried out. Eight primers selected from 40 random primers were used to amplify 45 tea samples, and a total of 95 DNA bands were amplified, of which 90 (94.7 %) were polymorphism. The average number of DNA bands amplified by each primer was 11.5. Based on the results of UPGMA cluster analysis of 95 DNA bands amplified by 8 primers,all the tested materials could be classified into 7 groups including 5 complex groups and 2 simple groups, which was basically identical with morphological classification. In addition, there were some speciations in 2 simple groups.

  13. Classifying the future of universes with dark energy

    International Nuclear Information System (INIS)

    We classify the future of the universe for general cosmological models including matter and dark energy. If the equation of state of dark energy is less then -1, the age of the universe becomes finite. We compute the rest of the age of the universe for such universe models. The behaviour of the future growth of matter density perturbation is also studied. We find that the collapse of the spherical overdensity region is greatly changed if the equation of state of dark energy is less than -1

  14. Some factors influencing interobserver variation in classifying simple pneumoconiosis.

    OpenAIRE

    Musch, D C; Higgins, I T; Landis, J R

    1985-01-01

    Three experienced physician readers assessed the chest radiographs of 743 men from a coal mining community in West Virginia for the signs of simple pneumoconiosis, using the ILO U/C 1971 Classification of Radiographs of the Pneumoconioses. The number of films categorised by each reader as showing evidence of simple pneumoconiosis varied from 63 (8.5%) to 114 (15.3%) of the 743 films classified. The effect of film quality and obesity on interobserver agreement was assessed by use of kappa-type...

  15. Brain Computer Interface. Comparison of Neural Networks Classifiers.

    OpenAIRE

    Martínez Pérez, Jose Luis; Barrientos Cruz, Antonio

    2008-01-01

    Brain Computer Interface is an emerging technology that allows new output paths to communicate the user’s intentions without use of normal output ways, such as muscles or nerves (Wolpaw, J. R.; et al., 2002).In order to obtain its objective BCI devices shall make use of classifier which translate the inputs provided by user’s brain signal to commands for external devices. The primary uses of this technology will benefit persons with some kind blocking disease as for example: ALS, brainstem st...

  16. Support vector machine classifiers for large data sets.

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-01-31

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

  17. Classifying Cubic Edge-Transitive Graphs of Order 8

    Indian Academy of Sciences (India)

    Mehdi Alaeiyan; M K Hosseinipoor

    2009-11-01

    A simple undirected graph is said to be semisymmetric if it is regular and edge-transitive but not vertex-transitive. Let be a prime. It was shown by Folkman (J. Combin. Theory 3(1967) 215--232) that a regular edge-transitive graph of order 2 or 22 is necessarily vertex-transitive. In this paper, an extension of his result in the case of cubic graphs is given. It is proved that, every cubic edge-transitive graph of order 8 is symmetric, and then all such graphs are classified.

  18. Information Forests

    CERN Document Server

    Yi, Zhao; Dewan, Maneesh; Zhan, Yiqiang

    2012-01-01

    We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-conditional distributions in the resulting partitions. The basic idea consists of deferring classification until a measure of "classification confidence" is sufficiently high, and instead breaking down the data so as to maximize this measure. In an alternative interpretation, Information Forests attempt to partition the data into subsets that are "as informative as possible" for the purpose of the task, which is to classify the data. Classification confidence, or informative content of the subsets, is quantified by the Information Divergence. Our approach relates to active learning, semi-supervised learning, mixed generative/discriminative learning.

  19. Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

    Indian Academy of Sciences (India)

    N S Nithya; K Duraiswamy

    2014-02-01

    The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is wellperformed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and membership functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.

  20. Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm

    OpenAIRE

    Zhao, Jiaqi; Fernandes, Vitor Basto; Jiao, Licheng; Yevseyeva, Iryna; Maulana, Asep; Li, Rui; Bäck, Thomas; Emmerich, Michael T. M.

    2014-01-01

    Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in the machine learning community to analyze the performance of parametric classifiers or sets of Pareto optimal classifiers. In order to directly compare two sets of classifiers the area (or volume) under the convex hull can be used as a scalar indicator for the performance of a set of classifiers in receiver operati...

  1. Classifying paragraph types using linguistic features: Is paragraph positioning important?

    Directory of Open Access Journals (Sweden)

    Scott A. Crossley, Kyle Dempsey & Danielle S. McNamara

    2011-12-01

    Full Text Available This study examines the potential for computational tools and human raters to classify paragraphs based on positioning. In this study, a corpus of 182 paragraphs was collected from student, argumentative essays. The paragraphs selected were initial, middle, and final paragraphs and their positioning related to introductory, body, and concluding paragraphs. The paragraphs were analyzed by the computational tool Coh-Metrix on a variety of linguistic features with correlates to textual cohesion and lexical sophistication and then modeled using statistical techniques. The paragraphs were also classified by human raters based on paragraph positioning. The performance of the reported model was well above chance and reported an accuracy of classification that was similar to human judgments of paragraph type (66% accuracy for human versus 65% accuracy for our model. The model's accuracy increased when longer paragraphs that provided more linguistic coverage and paragraphs judged by human raters to be of higher quality were examined. The findings support the notions that paragraph types contain specific linguistic features that allow them to be distinguished from one another. The finding reported in this study should prove beneficial in classroom writing instruction and in automated writing assessment.

  2. Automatic misclassification rejection for LDA classifier using ROC curves.

    Science.gov (United States)

    Menon, Radhika; Di Caterina, Gaetano; Lakany, Heba; Petropoulakis, Lykourgos; Conway, Bernard A; Soraghan, John J

    2015-08-01

    This paper presents a technique to improve the performance of an LDA classifier by determining if the predicted classification output is a misclassification and thereby rejecting it. This is achieved by automatically computing a class specific threshold with the help of ROC curves. If the posterior probability of a prediction is below the threshold, the classification result is discarded. This method of minimizing false positives is beneficial in the control of electromyography (EMG) based upper-limb prosthetic devices. It is hypothesized that a unique EMG pattern is associated with a specific hand gesture. In reality, however, EMG signals are difficult to distinguish, particularly in the case of multiple finger motions, and hence classifiers are trained to recognize a set of individual gestures. However, it is imperative that misclassifications be avoided because they result in unwanted prosthetic arm motions which are detrimental to device controllability. This warrants the need for the proposed technique wherein a misclassified gesture prediction is rejected resulting in no motion of the prosthetic arm. The technique was tested using surface EMG data recorded from thirteen amputees performing seven hand gestures. Results show the number of misclassifications was effectively reduced, particularly in cases with low original classification accuracy. PMID:26736304

  3. Gamma mixture classifier for plaque detection in intravascular ultrasonic images.

    Science.gov (United States)

    Vegas-Sánchez-Ferrero, Gonzalo; Seabra, José; Rodriguez-Leor, Oriol; Serrano-Vida, Angel; Aja-Fernández, Santiago; Palencia, César; Martín-Fernández, Marcos; Sanches, Joao

    2014-01-01

    Carotid and coronary vascular incidents are mostly caused by vulnerable plaques. Detection and characterization of vulnerable plaques are important for early disease diagnosis and treatment. For this purpose, the echomorphology and composition have been studied. Several distributions have been used to describe ultrasonic data depending on tissues, acquisition conditions, and equipment. Among them, the Rayleigh distribution is a one-parameter model used to describe the raw envelope RF ultrasound signal for its simplicity, whereas the Nakagami distribution (a generalization of the Rayleigh distribution) is the two-parameter model which is commonly accepted. However, it fails to describe B-mode images or Cartesian interpolated or subsampled RF images because linear filtering changes the statistics of the signal. In this work, a gamma mixture model (GMM) is proposed to describe the subsampled/interpolated RF images and it is shown that the parameters and coefficients of the mixture are useful descriptors of speckle pattern for different types of plaque tissues. This new model outperforms recently proposed probabilistic and textural methods with respect to plaque description and characterization of echogenic contents. Classification results provide an overall accuracy of 86.56% for four classes and 95.16% for three classes. These results evidence the classifier usefulness for plaque characterization. Additionally, the classifier provides probability maps according to each tissue type, which can be displayed for inspecting local tissue composition, or used for automatic filtering and segmentation. PMID:24402895

  4. Decision Tree Classifiers for Star/Galaxy Separation

    CERN Document Server

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

    2010-01-01

    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: $14\\le r\\le21$ ($85.2%$) and $r\\ge19$ ($82.1%$). 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. (2006). We find that our FT classifier is comparable or better in completeness over the full magnitude range $15\\le r\\le21$, with m...

  5. Elephants classify human ethnic groups by odor and garment color.

    Science.gov (United States)

    Bates, Lucy A; Sayialel, Katito N; Njiraini, Norah W; Moss, Cynthia J; Poole, Joyce H; Byrne, Richard W

    2007-11-20

    Animals can benefit from classifying predators or other dangers into categories, tailoring their escape strategies to the type and nature of the risk. Studies of alarm vocalizations have revealed various levels of sophistication in classification. In many taxa, reactions to danger are inflexible, but some species can learn the level of threat presented by the local population of a predator or by specific, recognizable individuals. Some species distinguish several species of predator, giving differentiated warning calls and escape reactions; here, we explore an animal's classification of subgroups within a species. We show that elephants distinguish at least two Kenyan ethnic groups and can identify them by olfactory and color cues independently. In the Amboseli ecosystem, Kenya, young Maasai men demonstrate virility by spearing elephants (Loxodonta africana), but Kamba agriculturalists pose little threat. Elephants showed greater fear when they detected the scent of garments previously worn by Maasai than by Kamba men, and they reacted aggressively to the color associated with Maasai. Elephants are therefore able to classify members of a single species into subgroups that pose different degrees of danger. PMID:17949977

  6. Deposition of Nanostructured Thin Film from Size-Classified Nanoparticles

    Science.gov (United States)

    Camata, Renato P.; Cunningham, Nicholas C.; Seol, Kwang Soo; Okada, Yoshiki; Takeuchi, Kazuo

    2003-01-01

    Materials comprising nanometer-sized grains (approximately 1_50 nm) exhibit properties dramatically different from those of their homogeneous and uniform counterparts. These properties vary with size, shape, and composition of nanoscale grains. Thus, nanoparticles may be used as building blocks to engineer tailor-made artificial materials with desired properties, such as non-linear optical absorption, tunable light emission, charge-storage behavior, selective catalytic activity, and countless other characteristics. This bottom-up engineering approach requires exquisite control over nanoparticle size, shape, and composition. We describe the design and characterization of an aerosol system conceived for the deposition of size classified nanoparticles whose performance is consistent with these strict demands. A nanoparticle aerosol is generated by laser ablation and sorted according to size using a differential mobility analyzer. Nanoparticles within a chosen window of sizes (e.g., (8.0 plus or minus 0.6) nm) are deposited electrostatically on a surface forming a film of the desired material. The system allows the assembly and engineering of thin films using size-classified nanoparticles as building blocks.

  7. Deep convolutional neural networks for classifying GPR B-scans

    Science.gov (United States)

    Besaw, Lance E.; Stimac, Philip J.

    2015-05-01

    Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

  8. Using Narrow Band Photometry to Classify Stars and Brown Dwarfs

    CERN Document Server

    Mainzer, A K; Sievers, J L; Young, E T; Lean, Ian S. Mc

    2004-01-01

    We present a new system of narrow band filters in the near infrared that can be used to classify stars and brown dwarfs. This set of four filters, spanning the H band, can be used to identify molecular features unique to brown dwarfs, such as H2O and CH4. The four filters are centered at 1.495 um (H2O), 1.595 um (continuum), 1.66 um (CH4), and 1.75 um (H2O). Using two H2O filters allows us to solve for individual objects' reddenings. This can be accomplished by constructing a color-color-color cube and rotating it until the reddening vector disappears. We created a model of predicted color-color-color values for different spectral types by integrating filter bandpass data with spectra of known stars and brown dwarfs. We validated this model by making photometric measurements of seven known L and T dwarfs, ranging from L1 - T7.5. The photometric measurements agree with the model to within +/-0.1 mag, allowing us to create spectral indices for different spectral types. We can classify A through early M stars to...

  9. Fuzzy classifier for fault diagnosis in analog electronic circuits.

    Science.gov (United States)

    Kumar, Ashwani; Singh, A P

    2013-11-01

    Many studies have presented different approaches for the fault diagnosis with fault models having ± 50% variation in the component values in analog electronic circuits. There is still a need of the approaches which provide the fault diagnosis with the variation in the component value below ± 50%. A new single and multiple fault diagnosis technique for soft faults in analog electronic circuit using fuzzy classifier has been proposed in this paper. This technique uses the simulation before test (SBT) approach by analyzing the frequency response of the analog circuit under faulty and fault free conditions. Three signature parameters peak gain, frequency and phase associated with peak gain, of the frequency response of the analog circuit are observed and extracted such that they give unique values for faulty and fault free configuration of the circuit. The single and double fault models with the component variations from ± 10% to ± 50% are considered. The fuzzy classifier along the classification of faults gives the estimated component value under faulty and faultfree conditions. The proposed method is validated using simulated data and the real time data for a benchmark analog circuit. The comparative analysis is also presented for both the validations. PMID:23849881

  10. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

    Science.gov (United States)

    Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

    2013-01-01

    Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

  11. Classified and Clustered Data Constellation: An Efficient Approach of 3D Urban Data Management

    DEFF Research Database (Denmark)

    Azri, Suhaibah; Ujang, Uznir; Antón Castro, Francesc;

    2016-01-01

    involves various types of data, such as multiple types of zoning themes in the case of urban mixed-use development. Thus, a special technique for efficient handling and management of urban data is necessary. This paper proposes a structure called Classified and Clustered Data Constellation (CCDC) for urban...... data management. CCDC operates on the basis of two filters: classification and clustering. To boost up the performance of information retrieval, CCDC offers a minimal percentage of overlap among nodes and coverage area to avoid repetitive data entry and multipath query. The results of tests conducted...... on several urban mixed-use development datasets using CCDC verify that it efficiently retrieves their semantic and spatial information. Further, comparisons conducted between CCDC and existing clustering and data constellation techniques, from the aspect of preservation of minimal overlap and...

  12. A HYBRID METHOD USING LEXICON-BASED APPROACH AND NAIVE BAYES CLASSIFIER FOR ARABIC OPINION QUESTION ANSWERING

    Directory of Open Access Journals (Sweden)

    Khalid Khalifa

    2014-01-01

    Full Text Available Opinion Question Answering (Opinion QA is the task of enabling users to explore others opinions toward a particular service of product in order to make decisions. Arabic Opinion QA is more challenging due to its complex morphology compared to other languages and has many varieties dialects. On the other hand, there are insignificant research efforts and resources available that focus on Opinion QA in Arabic. This study aims to address the difficulties of Arabic opinion QA by proposing a hybrid method of lexicon-based approach and classification using Naïve Bayes classifier. The proposed method contains pre-processing phases such as, transformation, normalization and tokenization and exploiting auxiliary information (thesaurus. The lexicon-based approach is executed by replacing some words with its synonyms using the domain dictionary. The classification task is performed by Naïve Bayes classifier to classify the opinions based on the positive or negative sentiment polarity. The proposed method has been evaluated using the common information retrieval metrics i.e., Precision, Recall and F-measure. For comparison, three classifiers have been applied which are Naïve Bayes (NB, Support Vector Machine (SVM and K-Nearest Neighbor (KNN. The experimental results have demonstrated that NB outperforms SVM and KNN by achieving 91% accuracy.

  13. Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

    Science.gov (United States)

    Eguizabal, Alma; Laughney, Ashley M.; Garcia Allende, Pilar Beatriz; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.; López-Higuera, José M.; Conde, Olga M.

    2013-02-01

    Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.

  14. 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.

  15. Discrimination of Mine Seismic Events and Blasts Using the Fisher Classifier, Naive Bayesian Classifier and Logistic Regression

    Science.gov (United States)

    Dong, Longjun; Wesseloo, Johan; Potvin, Yves; Li, Xibing

    2016-01-01

    Seismic events and blasts generate seismic waveforms that have different characteristics. The challenge to confidently differentiate these two signatures is complex and requires the integration of physical and statistical techniques. In this paper, the different characteristics of blasts and seismic events were investigated by comparing probability density distributions of different parameters. Five typical parameters of blasts and events and the probability density functions of blast time, as well as probability density functions of origin time difference for neighbouring blasts were extracted as discriminant indicators. The Fisher classifier, naive Bayesian classifier and logistic regression were used to establish discriminators. Databases from three Australian and Canadian mines were established for training, calibrating and testing the discriminant models. The classification performances and discriminant precision of the three statistical techniques were discussed and compared. The proposed discriminators have explicit and simple functions which can be easily used by workers in mines or researchers. Back-test, applied results, cross-validated results and analysis of receiver operating characteristic curves in different mines have shown that the discriminator for one of the mines has a reasonably good discriminating performance.

  16. Naive Bayes Classifier Algorithm Approach for Mapping Poor Families Potential

    Directory of Open Access Journals (Sweden)

    Sri Redjeki

    2015-12-01

    Full Text Available The poverty rate that was recorded high in Indonesia becomes main priority the government to find a solution to poverty rate was below 10%. Initial identification the potential poverty becomes a very important thing to anticipate the amount of the poverty rate. Naive Bayes Classifier (NBC algorithm was one of data mining algorithms that can be used to perform classifications the family poor with 11 indicators with three classifications. This study using sample data of poor families a total of 219 data. A system that built use Java programming compared to the result of Weka software with accuracy the results of classification of 93%. The results of classification data of poor families mapped by adding latitude-longitude data and a photograph of the house of the condition of poor families. Based on the results of mapping classifications using NBC can help the government in Kabupaten Bantul in examining the potential of poor people.

  17. Image replica detection based on support vector classifier

    Science.gov (United States)

    Maret, Y.; Dufaux, F.; Ebrahimi, T.

    2005-08-01

    In this paper, we propose a technique for image replica detection. By replica, we mean equivalent versions of a given reference image, e.g. after it has undergone operations such as compression, filtering or resizing. Applications of this technique include discovery of copyright infringement or detection of illicit content. The technique is based on the extraction of multiple features from an image, namely texture, color, and spatial distribution of colors. Similar features are then grouped into groups and the similarity between two images is given by several partial distances. The decision function to decide whether a test image is a replica of a given reference image is finally derived using Support Vector Classifier (SVC). In this paper, we show that this technique achieves good results on a large database of images. For instance, for a false negative rate of 5 % the system yields a false positive rate of only 6 " 10-5.

  18. Refining and classifying finite-time Lyapunov exponent ridges

    CERN Document Server

    Allshouse, Michael R

    2015-01-01

    While more rigorous and sophisticated methods for identifying Lagrangian based coherent structures exist, the finite-time Lyapunov exponent (FTLE) field remains a straightforward and popular method for gaining some insight into transport by complex, time-dependent two-dimensional flows. In light of its enduring appeal, and in support of good practice, we begin by investigating the effects of discretization and noise on two numerical approaches for calculating the FTLE field. A practical method to extract and refine FTLE ridges in two-dimensional flows, which builds on previous methods, is then presented. Seeking to better ascertain the role of an FTLE ridge in flow transport, we adapt an existing classification scheme and provide a thorough treatment of the challenges of classifying the types of deformation represented by an FTLE ridge. As a practical demonstration, the methods are applied to an ocean surface velocity field data set generated by a numerical model.

  19. Classifying transient signals with nonlinear dynamic filter banks

    International Nuclear Information System (INIS)

    In recent years, several specific advances in the study of chaotic processes have been made which appear to have immediate applicability to signal processing. This paper describes two applications of one of these advances, nonlinear modeling, to signal detection ampersand classification, in particular for short-lived or transient or signals. The first method uses the coefficients from an adaptively fit model as a set of features for signal detection and classification. In the second method, a library of predictive nonlinear dynamic equations is used as a filter bank, and statistics on the prediction residuals are used to form feature vectors for input data segments. These feature vectors provide a mechanism for detecting and classifying model transients at signal-to-noise ratios as low as -10 dB, even when the generating dynamics of the transient signals are not present in the filter bank. The second method and some validating experiments are described in detail. copyright 1996 American Institute of Physics

  20. Road network extraction in classified SAR images using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    肖志强; 鲍光淑; 蒋晓确

    2004-01-01

    Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.

  1. Early Detection of Breast Cancer using SVM Classifier Technique

    CERN Document Server

    Rejani, Y Ireaneus Anna

    2009-01-01

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

  2. A Speedy Cardiovascular Diseases Classifier Using Multiple Criteria Decision Analysis

    Directory of Open Access Journals (Sweden)

    Wah Ching Lee

    2015-01-01

    Full Text Available Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP-based multiple criteria decision analysis (MCDA to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.

  3. An Automated Acoustic System to Monitor and Classify Birds

    Directory of Open Access Journals (Sweden)

    Ho KC

    2006-01-01

    Full Text Available This paper presents a novel bird monitoring and recognition system in noisy environments. The project objective is to avoid bird strikes to aircraft. First, a cost-effective microphone dish concept (microphone array with many concentric rings is presented that can provide directional and accurate acquisition of bird sounds and can simultaneously pick up bird sounds from different directions. Second, direction-of-arrival (DOA and beamforming algorithms have been developed for the circular array. Third, an efficient recognition algorithm is proposed which uses Gaussian mixture models (GMMs. The overall system is suitable for monitoring and recognition for a large number of birds. Fourth, a hardware prototype has been built and initial experiments demonstrated that the array can acquire and classify birds accurately.

  4. Deep Feature Learning and Cascaded Classifier for Large Scale Data

    DEFF Research Database (Denmark)

    Prasoon, Adhish

    state-of-the-art method for cartilage segmentation using one stage nearest neighbour classifier. Our method achieved better results than the state-of-the-art method for tibial as well as femoral cartilage segmentation. The next main contribution of the thesis deals with learning features autonomously...... learning architecture that autonomously learns the features from the images is the main insight of this study. While training the convolutional neural networks for segmentation purposes, the commonly used cost function does not consider the labels of the neighbourhood pixels/voxels. We propose spatially......This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge...

  5. Classifying orbits in the restricted three-body problem

    CERN Document Server

    Zotos, Euaggelos E

    2015-01-01

    The case of the planar circular restricted three-body problem is used as a test field in order to determine the character of the orbits of a small body which moves under the gravitational influence of the two heavy primary bodies. We conduct a thorough numerical analysis on the phase space mixing by classifying initial conditions of orbits and distinguishing between three types of motion: (i) bounded, (ii) escape and (iii) collisional. The presented outcomes reveal the high complexity of this dynamical system. Furthermore, our numerical analysis shows a remarkable presence of fractal basin boundaries along all the escape regimes. Interpreting the collisional motion as leaking in the phase space we related our results to both chaotic scattering and the theory of leaking Hamiltonian systems. We also determined the escape and collisional basins and computed the corresponding escape/collisional times. We hope our contribution to be useful for a further understanding of the escape and collisional mechanism of orbi...

  6. Intermediaries in Bredon (Co)homology and Classifying Spaces

    CERN Document Server

    Dembegioti, Fotini; Talelli, Olympia

    2011-01-01

    For certain contractible G-CW-complexes and F a family of subgroups of G, we construct a spectral sequence converging to the F-Bredon cohomology of G with E1-terms given by the F-Bredon cohomology of the stabilizer subgroups. As applications, we obtain several corollaries concerning the cohomological and geometric dimensions of the classifying space for the family F. We also introduce a hierarchically defined class of groups which contains all countable elementary amenable groups and countable linear groups of characteristic zero, and show that if a group G is in this class, then G has finite F-Bredon (co)homological dimension if and only if G has jump F-Bredon (co)homology.

  7. Handwritten Bangla Alphabet Recognition using an MLP Based Classifier

    CERN Document Server

    Basu, Subhadip; Sarkar, Ram; Kundu, Mahantapas; Nasipuri, Mita; Basu, Dipak Kumar

    2012-01-01

    The work presented here involves the design of a Multi Layer Perceptron (MLP) based classifier for recognition of handwritten Bangla alphabet using a 76 element feature set Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten characters of Bangla alphabet includes 24 shadow features, 16 centroid features and 36 longest-run features. Recognition performances of the MLP designed to work with this feature set are experimentally observed as 86.46% and 75.05% on the samples of the training and the test sets respectively. The work has useful application in the development of a complete OCR system for handwritten Bangla text.

  8. Building multiclass classifiers for remote homology detection and fold recognition

    Directory of Open Access Journals (Sweden)

    Karypis George

    2006-10-01

    Full Text Available Abstract Background Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. Results We present a comprehensive evaluation of a number of methods for building SVM-based multiclass classification schemes in the context of the SCOP protein classification. These methods include schemes that directly build an SVM-based multiclass model, schemes that employ a second-level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. Conclusion Analyzing the performance achieved by the different approaches on four different datasets we show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to not only lead to lower error rates but also reduce the number of errors in which a superfamily is assigned to an entirely different fold and a fold is predicted as being from a different SCOP class. Our results also show that the limited size of the training data makes it hard to learn complex second-level models, and that models of moderate complexity lead to consistently better results.

  9. Effective Network Intrusion Detection using Classifiers Decision Trees and Decision rules

    Directory of Open Access Journals (Sweden)

    G.MeeraGandhi

    2010-11-01

    Full Text Available In the era of information society, computer networks and their related applications are the emerging technologies. Network Intrusion Detection aims at distinguishing the behavior of the network. As the network attacks have increased in huge numbers over the past few years, Intrusion Detection System (IDS is increasingly becoming a critical component to secure the network. Owing to large volumes of security audit data in a network in addition to intricate and vibrant properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this work, the field of machine learning attempts to characterize how such changes can occur by designing, implementing, running, and analyzing algorithms that can be run on computers. The discipline draws on ideas, with the goal of understanding the computational character of learning. Learning always occurs in the context of some performance task, and that a learning method should always be coupled with a performance element that uses the knowledge acquired during learning. In this research, machine learning is being investigated as a technique for making the selection, using as training data and their outcome. In this paper, we evaluate the performance of a set of classifier algorithms of rules (JRIP, Decision Tabel, PART, and OneR and trees (J48, RandomForest, REPTree, NBTree. Based on the evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The empirical simulation result shows the comparison between the noticeable performance improvements. The classification models were trained using the data collected from Knowledge Discovery Databases (KDD for Intrusion Detection. The trained models were then used for predicting the risk of the attacks in a web server environment or by any network administrator or any Security Experts. The

  10. 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...

  11. Computational classifiers for predicting the short-term course of Multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Comi Giancarlo

    2011-06-01

    Full Text Available Abstract Background The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity of clinical, imaging and motor evoked potentials (MEP for predicting the short-term prognosis of multiple sclerosis (MS. Methods We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1 expanded disability status scale (EDSS, 2 disability progression, and 3 new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. Results We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS, MRI lesion load and central motor conduction time (CMCT, were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT we developed a neural network (NNet that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80% for predicting the change in the EDSS two years later. Conclusions The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.

  12. 78 FR 5828 - Agency Information Collection Activities: Petition To Classify Orphan as an Immediate Relative...

    Science.gov (United States)

    2013-01-28

    ..., at 77 FR 65709, allowing for a 60-day public comment period. USCIS did receive two comments in.../ ] Dashboard.do, or call the USCIS National Customer Service Center at 1-800-375-5283. Written comments and... adult member (age 18 and older), who lives in the home of the prospective adoptive parent(s), except...

  13. Classifying regional development in Iran (Application of Composite Index Approach

    Directory of Open Access Journals (Sweden)

    A. Sharifzadeh

    2012-01-01

    Full Text Available Extended abstract1- IntroductionThe spatial economy of Iran, like that of so many other developing countries, is characterized by an uneven spatial pattern of economic activities. The problem of spatial inequality emerged when efficiency-oriented sectoral policies came into conflict with the spatial dimension of development (Atash, 1988. Due to this conflict, extreme imbalanced development in Iran was created. Moreover spatial uneven distribution of economic activities in Iran is unknown and incomplete. So, there is an urgent need for more efficient and effective design, targeting and implementing interventions to manage spatial imbalances in development. Hence, the identification of development patterns at spatial scale and the factors generating them can help improve planning if development programs are focused on removing the constraints adversely affecting development in potentially good areas. There is a need for research that would describe and explain the problem of spatial development patterns as well as proposal of possible strategies, which can be used to develop the country and reduce the spatial imbalances. The main objective of this research was to determine spatial economic development level in order to identify spatial pattern of development and explain determinants of such imbalance in Iran based on methodology of composite index of development. Then, Iran provinces were ranked and classified according to the calculated composite index. To collect the required data, census of 2006 and yearbook in various times were used. 2- Theoretical basesTheories of regional inequality as well as empirical evidence regarding actual trends at the national or international level have been discussed and debated in the economic literature for over three decades. Early debates concerning the impact of market mechanisms on regional inequality in the West (Myrdal, 1957 have become popular again in the 1990s. There is a conflict on probable outcomes

  14. 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.

  15. Preliminary analysis of the JAPE ground vehicle test data with an artificial neural network classifier

    Science.gov (United States)

    Larsen, Nathan F.; Carnes, Ben L.

    1993-01-01

    Remotely sensing and classifying military vehicles in a battlefield environment have been the source of much research over the past 20 years. The ability to know where threat vehicles are located is an obvious advantage to military personnel. In the past active methods of ground vehicle detection such as radar have been used, but with the advancement of technology to locate these active sensors, passive sensors are preferred. Passive sensors detect acoustic emissions, seismic movement, electromagnetic radiation, etc., produced by the target and use this information to describe it. Deriving the mathematical models to classify vehicles in this manner has been, and is, quite complex and not always reliable. However, with the resurgence of artificial neural network (ANN) research in the past few years, developing models for this work may be a thing of the past. Preliminary results from an ANN analysis to the tank signatures recorded at the Joint Acoustic Propagation Experiment (JAPE) at the US Army White Sands Missile Range, NM, in July 1991, are presented.

  16. Application of maximin correlation analysis to classifying protein environments for function prediction.

    Science.gov (United States)

    Lee, Taehoon; Min, Hyeyoung; Kim, Seung Jean; Yoon, Sungroh

    2010-09-17

    More and more protein structures are being discovered, but most of these still have little functional information. Based on the assumption that structural resemblance would lead to functional similarity, researchers computationally compare a new structure with functionally annotated structures, for high-throughput function prediction. The effectiveness of this approach depends critically upon the quality of comparison. In particular, robust classification often becomes difficult when a function class is an aggregate of multiple subclasses, as is the case with protein annotations. For such multiple-subclass classification problems, an optimal method termed the maximin correlation analysis (MCA) was proposed. However, MCA has never been applied to automated protein function prediction although MCA can minimize the misclassification risk in the correlation-based nearest neighbor classification, thus increasing classification accuracy. In this article, we apply MCA to classifying three-dimensional protein local environment data derived from a subset of the protein data bank (PDB). In our framework, the MCA-based classifier outperformed the compared alternatives by 7-19% and 6-27% in terms of average sensitivity and specificity, respectively. Given that correlation-based similarity measures have been widely used for mining protein data, we expect that MCA would be employed to enhance other types of automated function prediction methods. PMID:20719237

  17. Recognition of American Sign Language (ASL) Classifiers in a Planetarium Using a Head-Mounted Display

    Science.gov (United States)

    Hintz, Eric G.; Jones, Michael; Lawler, Jeannette; Bench, Nathan

    2015-01-01

    A traditional accommodation for the deaf or hard-of-hearing in a planetarium show is some type of captioning system or a signer on the floor. Both of these have significant drawbacks given the nature of a planetarium show. Young audience members who are deaf likely don't have the reading skills needed to make a captioning system effective. A signer on the floor requires light which can then splash onto the dome. We have examined the potential of using a Head-Mounted Display (HMD) to provide an American Sign Language (ASL) translation. Our preliminary test used a canned planetarium show with a pre-recorded sound track. Since many astronomical objects don't have official ASL signs, the signer had to use classifiers to describe the different objects. Since these are not official signs, these classifiers provided a way to test to see if students were picking up the information using the HMD.We will present results that demonstrate that the use of HMDs is at least as effective as projecting a signer on the dome. This also showed that the HMD could provide the necessary accommodation for students for whom captioning was ineffective. We will also discuss the current effort to provide a live signer without the light splash effect and our early results on teaching effectiveness with HMDs.This work is partially supported by funding from the National Science Foundation grant IIS-1124548 and the Sorenson Foundation.

  18. Development of The Viking Speech Scale to classify the speech of children with cerebral palsy.

    Science.gov (United States)

    Pennington, Lindsay; Virella, Daniel; Mjøen, Tone; da Graça Andrada, Maria; Murray, Janice; Colver, Allan; Himmelmann, Kate; Rackauskaite, Gija; Greitane, Andra; Prasauskiene, Audrone; Andersen, Guro; de la Cruz, Javier

    2013-10-01

    Surveillance registers monitor the prevalence of cerebral palsy and the severity of resulting impairments across time and place. The motor disorders of cerebral palsy can affect children's speech production and limit their intelligibility. We describe the development of a scale to classify children's speech performance for use in cerebral palsy surveillance registers, and its reliability across raters and across time. Speech and language therapists, other healthcare professionals and parents classified the speech of 139 children with cerebral palsy (85 boys, 54 girls; mean age 6.03 years, SD 1.09) from observation and previous knowledge of the children. Another group of health professionals rated children's speech from information in their medical notes. With the exception of parents, raters reclassified children's speech at least four weeks after their initial classification. Raters were asked to rate how easy the scale was to use and how well the scale described the child's speech production using Likert scales. Inter-rater reliability was moderate to substantial (k>.58 for all comparisons). Test-retest reliability was substantial to almost perfect for all groups (k>.68). Over 74% of raters found the scale easy or very easy to use; 66% of parents and over 70% of health care professionals judged the scale to describe children's speech well or very well. We conclude that the Viking Speech Scale is a reliable tool to describe the speech performance of children with cerebral palsy, which can be applied through direct observation of children or through case note review. PMID:23891732

  19. Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank

    Directory of Open Access Journals (Sweden)

    Md.Mahbubur Rahman

    2014-07-01

    Full Text Available Bank plays the central role for the economic development world-wide. The failure and success of the banking sector depends upon the ability to proper evaluation of credit risk. Credit risk evaluation of any potential credit application has remained a challenge for banks all over the world till today. Artificial neural network plays a tremendous role in the field of finance for making critical, enigmatic and sensitive decisions those are sometimes impossible for human being. Like other critical decision in the finance, the decision of sanctioning loan to the customer is also an enigmatic problem. The objective of this paper is to design such a Neural Network that can facilitate loan officers to make correct decision for providing loan to the proper client. This paper checks the applicability of one of the new integrated model with nearest neighbor classifier on a sample data taken from a Bangladeshi Bank named Brac Bank. The Neural network will consider several factors of the client of the bank and make the loan officer informed about client’s eligibility of getting a loan. Several effective methods of neural network can be used for making this bank decision such as back propagation learning, regression model, gradient descent algorithm, nearest neighbor classifier etc.

  20. Safety assessment of plant varieties using transcriptomics profiling and a one-class classifier.

    Science.gov (United States)

    van Dijk, Jeroen P; de Mello, Carla Souza; Voorhuijzen, Marleen M; Hutten, Ronald C B; Arisi, Ana Carolina Maisonnave; Jansen, Jeroen J; Buydens, Lutgarde M C; van der Voet, Hilko; Kok, Esther J

    2014-10-01

    An important part of the current hazard identification of novel plant varieties is comparative targeted analysis of the novel and reference varieties. Comparative analysis will become much more informative with unbiased analytical approaches, e.g. omics profiling. Data analysis estimating the similarity of new varieties to a reference baseline class of known safe varieties would subsequently greatly facilitate hazard identification. Further biological and eventually toxicological analysis would then only be necessary for varieties that fall outside this reference class. For this purpose, a one-class classifier tool was explored to assess and classify transcriptome profiles of potato (Solanum tuberosum) varieties in a model study. Profiles of six different varieties, two locations of growth, two year of harvest and including biological and technical replication were used to build the model. Two scenarios were applied representing evaluation of a 'different' variety and a 'similar' variety. Within the model higher class distances resulted for the 'different' test set compared with the 'similar' test set. The present study may contribute to a more global hazard identification of novel plant varieties. PMID:25046166

  1. 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. PMID:25680220

  2. 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.

  3. Analyzing tree-shape anatomical structures using topological descriptors of branching and ensemble of classifiers

    Directory of Open Access Journals (Sweden)

    Angeliki Skoura

    2013-04-01

    Full Text Available The analysis of anatomical tree-shape structures visualized in medical images provides insight into the relationship between tree topology and pathology of the corresponding organs. In this paper, we propose three methods to extract descriptive features of the branching topology; the asymmetry index, the encoding of branching patterns using a node labeling scheme and an extension of the Sholl analysis. Based on these descriptors, we present classification schemes for tree topologies with respect to the underlying pathology. Moreover, we present a classifier ensemble approach which combines the predictions of the individual classifiers to optimize the classification accuracy. We applied the proposed methodology to a dataset of x-ray galactograms, medical images which visualize the breast ductal tree, in order to recognize images with radiological findings regarding breast cancer. The experimental results demonstrate the effectiveness of the proposed framework compared to state-of-the-art techniques suggesting that the proposed descriptors provide more valuable information regarding the topological patterns of ductal trees and indicating the potential of facilitating early breast cancer diagnosis.

  4. 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.

  5. MISR Level 2 FIRSTLOOK TOA/Cloud Classifier parameters V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This is the Level 2 FIRSTLOOK TOA/Cloud Classifiers Product. It contains the Angular Signature Cloud Mask (ASCM), Cloud Classifiers, and Support Vector Machine...

  6. Gaussian and feed-forward neural network classifiers for shower recognition, generalization and parallel implementation

    International Nuclear Information System (INIS)

    The performance of Gaussian and feed-forward neural network classifiers, is compared with respect to the recognition of energy deposition patterns in a calorimeter. Implementation aspects of these classifiers for a multi-processor architecture are discussed

  7. Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier.

    Science.gov (United States)

    Chan, Ian; Wells, William; Mulkern, Robert V; Haker, Steven; Zhang, Jianqing; Zou, Kelly H; Maier, Stephan E; Tempany, Clare M C

    2003-09-01

    A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor "ground truth." Textural features were extracted from the images using co-occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood (ML) classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine (SVM) and Fisher linear discriminant (FLD), utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone (PZ) of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(+/-0.064), and our best SVM classifier achieved an average ROC area of 0.761(+/-0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(+/-0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance (P=0.0003 and 0.0017, respectively) from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary

  8. Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier

    International Nuclear Information System (INIS)

    A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor ''ground truth.'' Textural features were extracted from the images using co-occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood (ML) classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine (SVM) and Fisher linear discriminant (FLD), utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone (PZ) of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(±0.064), and our best SVM classifier achieved an average ROC area of 0.761(±0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(±0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance (P=0.0003 and 0.0017, respectively) from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary

  9. The EB Factory Project I. A Fast, Neural Net Based, General Purpose Light Curve Classifier Optimized for Eclipsing Binaries

    CERN Document Server

    Paegert, M; Burger, D 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 LSST. 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,...

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

    OpenAIRE

    Israël, Menno; Broek, van den, L.A.M.; Putten, van, B.; Khan, L.; Petrushin, V.A.

    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 two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classific...

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

    OpenAIRE

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

    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...

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

    OpenAIRE

    Huiyan Jiang; Di Zhao; Tianjiao Feng; Shiyang Liao; Yenwei Chen

    2013-01-01

    A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA). In order to verify the effectiveness of th...

  13. A Classifier Fusion System with Verification Module for Improving Recognition Reliability

    OpenAIRE

    Zhang, Ping

    2010-01-01

    In this paper, we proposed a novel classifier fusion system to congregate the recognition results of an ANN classifier and a modified KNN classifier. The recognition results are verified by the recognition results of SVM. As two entirely different classification techniques (image-based OCR and 1-D digital signal SVM classification) are applied to the system, experiments have demonstrated that the proposed classifier fusion system with SVM verification module can significantly increase the sys...

  14. Classifying and explaining democracy in the Muslim world

    Directory of Open Access Journals (Sweden)

    Rohaizan Baharuddin

    2012-12-01

    Full Text Available The purpose of this study is to classify and explain democracies in the 47 Muslim countries between the years 1998 and 2008 by using liberties and elections as independent variables. Specifically focusing on the context of the Muslim world, this study examines the performance of civil liberties and elections, variation of democracy practised the most, the elections, civil liberties and democratic transitions and patterns that followed. Based on the quantitative data primarily collected from Freedom House, this study demonstrates the following aggregate findings: first, the “not free not fair” elections, the “limited” civil liberties and the “Illiberal Partial Democracy” were the dominant feature of elections, civil liberties and democracy practised in the Muslim world; second, a total of 413 Muslim regimes out of 470 (47 regimes x 10 years remained the same as their democratic origin points, without any transitions to a better or worse level of democracy, throughout these 10 years; and third, a slow, yet steady positive transition of both elections and civil liberties occurred in the Muslim world with changes in the nature of elections becoming much more progressive compared to the civil liberties’ transitions.

  15. Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

    LENUS (Irish Health Repository)

    Dakna, Mohammed

    2010-12-10

    Abstract Background The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School. Results We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential. Conclusions Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

  16. A Novel Performance Metric for Building an Optimized Classifier

    Directory of Open Access Journals (Sweden)

    Mohammad Hossin

    2011-01-01

    Full Text Available Problem statement: Typically, the accuracy metric is often applied for optimizing the heuristic or stochastic classification models. However, the use of accuracy metric might lead the searching process to the sub-optimal solutions due to its less discriminating values and it is also not robust to the changes of class distribution. Approach: To solve these detrimental effects, we propose a novel performance metric which combines the beneficial properties of accuracy metric with the extended recall and precision metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP. Results: In this study, we demonstrate that the OARP metric is theoretically better than the accuracy metric using four generated examples. We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the conventional accuracy metric. Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the accuracy metric alone for all binary data sets. Conclusion: The experiments have proved that the OARP metric leads stochastic classifiers such as the MCS towards a better training model, which in turn will improve the predictive results of any heuristic or stochastic classification models.

  17. An automated approach to the design of decision tree classifiers

    Science.gov (United States)

    Argentiero, P.; Chin, P.; Beaudet, P.

    1980-01-01

    The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

  18. Impacts of classifying New York City students as overweight.

    Science.gov (United States)

    Almond, Douglas; Lee, Ajin; Schwartz, Amy Ellen

    2016-03-29

    US schools increasingly report body mass index (BMI) to students and their parents in annual fitness "report cards." We obtained 3,592,026 BMI reports for New York City public school students for 2007-2012. We focus on female students whose BMI puts them close to their age-specific cutoff for categorization as overweight. Overweight students are notified that their BMI "falls outside a healthy weight" and they should review their BMI with a health care provider. Using a regression discontinuity design, we compare those classified as overweight but near to the overweight cutoff to those whose BMI narrowly earned them a "healthy" BMI grouping. We find that overweight categorization generates small impacts on girls' subsequent BMI and weight. Whereas presumably an intent of BMI report cards was to slow BMI growth among heavier students, BMIs and weights did not decline relative to healthy peers when assessed the following academic year. Our results speak to the discrete categorization as overweight for girls with BMIs near the overweight cutoff, not to the overall effect of BMI reporting in New York City. PMID:26976566

  19. A dimensionless parameter for classifying hemodynamics in intracranial

    Science.gov (United States)

    Asgharzadeh, Hafez; Borazjani, Iman

    2015-11-01

    Rupture of an intracranial aneurysm (IA) is a disease with high rates of mortality. Given the risk associated with the aneurysm surgery, quantifying the likelihood of aneurysm rupture is essential. There are many risk factors that could be implicated in the rupture of an aneurysm. However, the most important factors correlated to the IA rupture are hemodynamic factors such as wall shear stress (WSS) and oscillatory shear index (OSI) which are affected by the IA flows. Here, we carry out three-dimensional high resolution simulations on representative IA models with simple geometries to test a dimensionless number (first proposed by Le et al., ASME J Biomech Eng, 2010), denoted as An number, to classify the flow mode. An number is defined as the ratio of the time takes the parent artery flow transports across the IA neck to the time required for vortex ring formation. Based on the definition, the flow mode is vortex if An>1 and it is cavity if AnOSI on the human subject IA. This work was supported partly by the NIH grant R03EB014860, and the computational resources were partly provided by CCR at UB. We thank Prof. Hui Meng and Dr. Jianping Xiang for providing us the database of aneurysms and helpful discussions.

  20. Hyperspectral image classifier based on beach spectral feature

    International Nuclear Information System (INIS)

    The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

  1. Bilayer segmentation of webcam videos using tree-based classifiers.

    Science.gov (United States)

    Yin, Pei; Criminisi, Antonio; Winn, John; Essa, Irfan

    2011-01-01

    This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a novel motion representation, referred to as "motons," inspired by research in object recognition. Second, we propose estimating the segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo systems. PMID:21088317

  2. Self-organised clustering for road extraction in classified imagery

    Science.gov (United States)

    Doucette, Peter; Agouris, Peggy; Stefanidis, Anthony; Musavi, Mohamad

    The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.

  3. Linearly and Quadratically Separable Classifiers Using Adaptive Approach

    Institute of Scientific and Technical Information of China (English)

    Mohamed Abdel-Kawy Mohamed Ali Soliman; Rasha M. Abo-Bakr

    2011-01-01

    This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in Rn.In each iteration,a subset of the sampling data (n-points,where n is the number of features) is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin e and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.

  4. Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort.

    Science.gov (United States)

    Frey, Jérémy; Appriou, Aurélien; Lotte, Fabien; Hachet, Martin

    2016-01-01

    With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users' states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials' (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users' state by modifying the overlap of left and right images according to the classifier output. PMID:26819580

  5. Executed Movement Using EEG Signals through a Naive Bayes Classifier

    Directory of Open Access Journals (Sweden)

    Juliano Machado

    2014-11-01

    Full Text Available Recent years have witnessed a rapid development of brain-computer interface (BCI technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA and the naive Bayes (NB classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.

  6. Two-categorical bundles and their classifying spaces

    DEFF Research Database (Denmark)

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

    2012-01-01

    For a 2-category 2C we associate a notion of a principal 2C-bundle. In case of the 2-category of 2-vector spaces in the sense of M.M. Kapranov and V.A. Voevodsky this gives the the 2-vector bundles of N.A. Baas, B.I. Dundas and J. Rognes. Our main result says that the geometric nerve of a good 2......-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...... on the main theorem shows that the principal 2-bundles associated to the 2-category of 2-vector spaces in the sense of J.C. Baez and A.S. Crans split, up to concordance, as two copies of ordinary vector bundles. When 2C is a cobordism type 2-category we get a new notion of cobordism-bundles which turns out...

  7. On Classifying the Divisor Involutions in Calabi-Yau Threefolds

    CERN Document Server

    Gao, Xin

    2013-01-01

    In order to support the odd moduli in models of (type IIB) string compactification, we classify the Calabi-Yau threefolds with h^{1,1}<=4 which exhibit pairs of identical divisors, with different line-bundle charges, mapping to each other under possible divisor exchange involutions. For this purpose, the divisors of interest are identified as completely rigid surface, Wilson surface, K3 surface and some other deformation surfaces. Subsequently, various possible exchange involutions are examined under the symmetry of Stanley-Reisner Ideal. In addition, we search for the Calabi-Yau theefolds which contain a divisor with several disjoint components. Under certain reflection involution, such spaces also have nontrivial odd components in (1,1)-cohomology class. String compactifications on such Calabi-Yau orientifolds with non-zero h^{1,1}_-(CY_3/\\sigma) could be promising for concrete model building in both particle physics and cosmology. In the spirit of using such Calabi-Yau orientifolds in the context of LAR...

  8. Potential of Spectroradiometry to Classify Soil Clay Content

    Directory of Open Access Journals (Sweden)

    André Carnieletto Dotto

    2016-01-01

    Full Text Available ABSTRACT Diffuse reflectance spectroscopy (DRS is a fast and cheap alternative for soil clay, but needs further investigation to assess the scope of application. The purpose of the study was to develop a linear regression model to predict clay content from DRS data, to classify the soils into three textural classes, similar to those defined by a regulation of the Brazilian Ministry of Agriculture, Livestock and Food Supply. The DRS data of 412 soil samples, from the 0.0-0.5 m layer, from different locations in the state of Rio Grande do Sul, Brazil, were measured at wavelengths of 350 to 2,500 nm in the laboratory. The fitting of the linear regression model developed to predict soil clay content from the DRS data was based on a R2 value of 0.74 and 0.75, with a RMSE of 7.82 and 8.51 % for the calibration and validation sets, respectively. Soil texture classification had an overall accuracy of 79.0 % (calibration and 80.9 % (validation. The heterogeneity of soil samples affected the performance of the prediction models. Future studies should consider a previous classification of soil samples in different groups by soil type, parent material and/or sampling region.

  9. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  10. 41 CFR 102-34.45 - How are passenger automobiles classified?

    Science.gov (United States)

    2010-07-01

    ... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class...

  11. Boosting 2-Thresholded Weak Classifiers over Scattered Rectangle Features for Object Detection

    Directory of Open Access Journals (Sweden)

    Weize Zhang

    2009-12-01

    Full Text Available In this paper, we extend Viola and Jones’ detection framework in two aspects. Firstly, by removing the restriction of the geometry adjacency rule over Haarlike feature, we get a richer representation called scattered rectangle feature, which explores much more orientations other than horizontal, vertical and diagonal, as well as misaligned, detached and non-rectangle shape information that is unreachable to Haar-like feature. Secondly, we strengthen the discriminating power of the weak classifiers by expanding them into 2-thresholded ones, which guarantees a better classification with smaller error, by the simple motivation that the bound on the accuracy of the final hypothesis improves when any of the weak hypotheses is improved. An optimal linear online algorithm is also proposed to determine the two thresholds. The comparison experiments on MIT+CMU upright face test set under an objective detection criterion show that the extended method outperforms the original one.

  12. A clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens.

    Science.gov (United States)

    Hall, Bonnie; Chen, Wenjin; Reiss, Michael; Foran, David J

    2007-01-01

    Motivated by the current limitations of automated quantitative image analysis in discriminating among intracellular immunohistochemical (IHC) staining patterns, this paper presents a two-fold approach for IHC characterization that utilizes both the protein stain information and the surrounding tissue architecture. Through the use of a color unmixing algorithm, stained tissue sections are automatically decomposed into the IHC stain, which visualizes the target protein, and the counterstain which provides an objective indication of the underlying histologic architecture. Feature measures are subsequently extracted from both staining planes. In order to characterize the IHC expression pattern, this approach exploits the use of a non-traditional feature based on textons. Novel biologically motivated filter banks are introduced in order to derive texture signatures for different IHC staining patterns. Systematic experiments using this approach were used to classify breast cancer tissue microarrays which had been previously prepared using immuno-targeted nuclear, cytoplasmic, and membrane stains. PMID:18044580

  13. Model-based immunization information routing.

    OpenAIRE

    Wang, D.; Jenders, R. A.

    2000-01-01

    We have developed a model for clinical information routing within an immunization registry. Components in this model include partners, contents and mechanisms. Partners are classified into senders, receivers and intermediates. Contents are classified into core contents and management information. Mechanisms are classified into topological control, temporal control, process control and communication channel control. Immunization reminders, forecasts and recalls in e-mail, fax and regular mail ...

  14. 5 CFR 1312.5 - Authority to classify.

    Science.gov (United States)

    2010-01-01

    ... Affairs. (iii) Deputy Associate Director for Energy and Science. (b) Classification authority is not... 1312.5 Administrative Personnel OFFICE OF MANAGEMENT AND BUDGET OMB DIRECTIVES CLASSIFICATION, DOWNGRADING, DECLASSIFICATION AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION Classification...

  15. Information barriers

    International Nuclear Information System (INIS)

    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

  16. 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.

  17. Pattern recognition applied to seismic signals of Llaima volcano (Chile): An evaluation of station-dependent classifiers

    Science.gov (United States)

    Curilem, Millaray; Huenupan, Fernando; Beltrán, Daniel; San Martin, Cesar; Fuentealba, Gustavo; Franco, Luis; Cardona, Carlos; Acuña, Gonzalo; Chacón, Max; Khan, M. Salman; Becerra Yoma, Nestor

    2016-04-01

    Automatic pattern recognition applied to seismic signals from volcanoes may assist seismic monitoring by reducing the workload of analysts, allowing them to focus on more challenging activities, such as producing reports, implementing models, and understanding volcanic behaviour. In a previous work, we proposed a structure for automatic classification of seismic events in Llaima volcano, one of the most active volcanoes in the Southern Andes, located in the Araucanía Region of Chile. A database of events taken from three monitoring stations on the volcano was used to create a classification structure, independent of which station provided the signal. The database included three types of volcanic events: tremor, long period, and volcano-tectonic and a contrast group which contains other types of seismic signals. In the present work, we maintain the same classification scheme, but we consider separately the stations information in order to assess whether the complementary information provided by different stations improves the performance of the classifier in recognising seismic patterns. This paper proposes two strategies for combining the information from the stations: i) combining the features extracted from the signals from each station and ii) combining the classifiers of each station. In the first case, the features extracted from the signals from each station are combined forming the input for a single classification structure. In the second, a decision stage combines the results of the classifiers for each station to give a unique output. The results confirm that the station-dependent strategies that combine the features and the classifiers from several stations improves the classification performance, and that the combination of the features provides the best performance. The results show an average improvement of 9% in the classification accuracy when compared with the station-independent method.

  18. 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

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

    International Nuclear Information System (INIS)

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

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

    Science.gov (United States)

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

    2012-02-01

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

  1. CLASSIFYING BENIGN AND MALIGNANT MASSES USING STATISTICAL MEASURES

    Directory of Open Access Journals (Sweden)

    B. Surendiran

    2011-11-01

    Full Text Available Breast cancer is the primary and most common disease found in women which causes second highest rate of death after lung cancer. The digital mammogram is the X-ray of breast captured for the analysis, interpretation and diagnosis. According to Breast Imaging Reporting and Data System (BIRADS benign and malignant can be differentiated using its shape, size and density, which is how radiologist visualize the mammograms. According to BIRADS mass shape characteristics, benign masses tend to have round, oval, lobular in shape and malignant masses are lobular or irregular in shape. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure to differentiate various shapes. In this paper, the malignant and benign masses present in mammogram are classified using Hue, Saturation and Value (HSV weight function based statistical measures. The weight function is robust against noise and captures the degree of gray content of the pixel. The statistical measures use gray weight value instead of gray pixel value to effectively discriminate masses. The 233 mammograms from the Digital Database for Screening Mammography (DDSM benchmark dataset have been used. The PASW data mining modeler has been used for constructing Neural Network for identifying importance of statistical measures. Based on the obtained important statistical measure, the C5.0 tree has been constructed with 60-40 data split. The experimental results are found to be encouraging. Also, the results will agree to the standard specified by the American College of Radiology-BIRADS Systems.

  2. Development of multicriteria models to classify energy efficiency alternatives

    International Nuclear Information System (INIS)

    This paper aims at describing a novel constructive approach to develop decision support models to classify energy efficiency initiatives, including traditional Demand-Side Management and Market Transformation initiatives, overcoming the limitations and drawbacks of Cost-Benefit Analysis. A multicriteria approach based on the ELECTRE-TRI method is used, focusing on four perspectives: - an independent Agency with the aim of promoting energy efficiency; - Distribution-only utilities under a regulated framework; - the Regulator; - Supply companies in a competitive liberalized market. These perspectives were chosen after a system analysis of the decision situation regarding the implementation of energy efficiency initiatives, looking for the main roles and power relations, with the purpose of structuring the decision problem by identifying the actors, the decision makers, the decision paradigm, and the relevant criteria. The multicriteria models developed allow considering different kinds of impacts, but avoiding difficult measurements and unit conversions due to the nature of the multicriteria method chosen. The decision is then based on all the significant effects of the initiative, both positive and negative ones, including ancillary effects often forgotten in cost-benefit analysis. The ELECTRE-TRI, as most multicriteria methods, provides to the Decision Maker the ability of controlling the relevance each impact can have on the final decision. The decision support process encompasses a robustness analysis, which, together with a good documentation of the parameters supplied into the model, should support sound decisions. The models were tested with a set of real-world initiatives and compared with possible decisions based on Cost-Benefit analysis

  3. A novel clinical tool to classify facioscapulohumeral muscular dystrophy phenotypes.

    Science.gov (United States)

    Ricci, Giulia; Ruggiero, Lucia; Vercelli, Liliana; Sera, Francesco; Nikolic, Ana; Govi, Monica; Mele, Fabiano; Daolio, Jessica; Angelini, Corrado; Antonini, Giovanni; Berardinelli, Angela; Bucci, Elisabetta; Cao, Michelangelo; D'Amico, Maria Chiara; D'Angelo, Grazia; Di Muzio, Antonio; Filosto, Massimiliano; Maggi, Lorenzo; Moggio, Maurizio; Mongini, Tiziana; Morandi, Lucia; Pegoraro, Elena; Rodolico, Carmelo; Santoro, Lucio; Siciliano, Gabriele; Tomelleri, Giuliano; Villa, Luisa; Tupler, Rossella

    2016-06-01

    Based on the 7-year experience of the Italian Clinical Network for FSHD, we revised the FSHD clinical form to describe, in a harmonized manner, the phenotypic spectrum observed in FSHD. The new Comprehensive Clinical Evaluation Form (CCEF) defines various clinical categories by the combination of different features. The inter-rater reproducibility of the CCEF was assessed between two examiners using kappa statistics by evaluating 56 subjects carrying the molecular marker used for FSHD diagnosis. The CCEF classifies: (1) subjects presenting facial and scapular girdle muscle weakness typical of FSHD (category A, subcategories A1-A3), (2) subjects with muscle weakness limited to scapular girdle or facial muscles (category B subcategories B1, B2), (3) asymptomatic/healthy subjects (category C, subcategories C1, C2), (4) subjects with myopathic phenotype presenting clinical features not consistent with FSHD canonical phenotype (D, subcategories D1, D2). The inter-rater reliability study showed an excellent concordance of the final four CCEF categories with a κ equal to 0.90; 95 % CI (0.71; 0.97). Absolute agreement was observed for categories C and D, an excellent agreement for categories A [κ = 0.88; 95 % CI (0.75; 1.00)], and a good agreement for categories B [κ = 0.79; 95 % CI (0.57; 1.00)]. The CCEF supports the harmonized phenotypic classification of patients and families. The categories outlined by the CCEF may assist diagnosis, genetic counseling and natural history studies. Furthermore, the CCEF categories could support selection of patients in randomized clinical trials. This precise categorization might also promote the search of genetic factor(s) contributing to the phenotypic spectrum of disease. PMID:27126453

  4. VIRTUAL MINING MODEL FOR CLASSIFYING TEXT USING UNSUPERVISED LEARNING

    Directory of Open Access Journals (Sweden)

    S. Koteeswaran

    2014-01-01

    Full Text Available In real world data mining is emerging in various era, one of its most outstanding performance is held in various research such as Big data, multimedia mining, text mining etc. Each of the researcher proves their contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are related to the problem definition of particular field of domain. Whereas the implementation model derives some sort of knowledge from the real time decision making behaviour such as artificial intelligence and swarm intelligence and has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of dataset and attributes. This knowledge is applied to the concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textual documents, text mining is applied. The text mining is the sub-domain in data mining. In text mining, the proposed Virtual Mining Model (VMM is defined for effective text clustering. This VMM involves the learning of conceptual terms; these terms are grouped in Significant Term List (STL. VMM model is appropriate combination of layer 1 arch with ABI (Analysis of Bilateral Intelligence. The frequent update of conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorithm is used for learning texual pattern in the Virtual Mining Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning algorithm.

  5. Intermediate depth burial of classified transuranic wastes in arid alluvium

    International Nuclear Information System (INIS)

    Intermediate depth disposal operations were conducted by the US Department of Energy (DOE) at the DOE's Nevada Test Site (NTS) from 1984 through 1989. These operations emplaced high-specific activity low-level wastes (LLW) and limited quantities of classified transuranic (TRU) wastes in 37 m (120-ft) deep, Greater Confinement Disposal (GCD) boreholes. The GCD boreholes are 3 m (10 ft) in diameter and founded in a thick sequence of arid alluvium. The bottom 15 m (50 ft) of each borehole was used for waste emplacement and the upper 21 m (70 ft) was backfilled with native alluvium. The bottom of each GCD borehole is almost 200 m (650 ft) above the water table. The GCD boreholes are located in one of the most arid portions of the US, with an average precipitation of 13 cm (5 inches) per year. The limited precipitation, coupled with generally warm temperatures and low humidities results in a hydrologic system dominated by evapotranspiration. The US Environmental Protection Agency's (EPA's) 40 CFR 191 defines the requirements for protection of human health from disposed TRU wastes. This EPA standard sets a number of requirements, including probabilistic limits on the cumulative releases of radionuclides to the accessible environment for 10,000 years. The DOE Nevada Operations Office (DOE/NV) has contracted with Sandia National Laboratories (Sandia) to conduct a performance assessment (PA) to determine if the TRU wastes emplaced in the GCD boreholes complies with the EPA's 40 CFR 191 requirements. This paper describes DOE's actions undertaken to evaluate whether the TRU wastes in the GCD boreholes will, or will not, endanger human health. Based on preliminary modeling, the TRU wastes in the GCD boreholes meet the EPA's requirements, and are, therefore, protective of human health

  6. Classifying X-Ray Binaries: A Probabilistic Approach

    Science.gov (United States)

    Gopalan, Giri; Dil Vrtilek, Saeqa; Bornn, Luke

    2015-08-01

    In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide whether the compact object is a black hole or a neutron star. To assist in this process, we develop a Bayesian statistical model that makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a three-dimensional coordinate system derived from X-ray spectral data where the first coordinate is the ratio of counts in the mid- to low-energy band (color 1), the second coordinate is the ratio of counts in the high- to low-energy band (color 2), and the third coordinate is the sum of counts in all three bands. We use this model to estimate the probabilities of an X-ray binary system containing a black hole, non-pulsing neutron star, or pulsing neutron star. In particular, we utilize a latent variable model in which the latent variables follow a Gaussian process prior distribution, and hence we are able to induce the spatial correlation which we believe exists between systems of the same type. The utility of this approach is demonstrated by the accurate prediction of system types using Rossi X-ray Timing Explorer All Sky Monitor data, but it is not flawless. In particular, non-pulsing neutron systems containing “bursters” that are close to the boundary demarcating systems containing black holes tend to be classified as black hole systems. As a byproduct of our analyses, we provide the astronomer with the public R code which can be used to predict the compact object type of XRBs given training data.

  7. A dimensionless parameter for classifying hemodynamics in intracranial

    Science.gov (United States)

    Asgharzadeh, Hafez; Borazjani, Iman

    2015-11-01

    Rupture of an intracranial aneurysm (IA) is a disease with high rates of mortality. Given the risk associated with the aneurysm surgery, quantifying the likelihood of aneurysm rupture is essential. There are many risk factors that could be implicated in the rupture of an aneurysm. However, the most important factors correlated to the IA rupture are hemodynamic factors such as wall shear stress (WSS) and oscillatory shear index (OSI) which are affected by the IA flows. Here, we carry out three-dimensional high resolution simulations on representative IA models with simple geometries to test a dimensionless number (first proposed by Le et al., ASME J Biomech Eng, 2010), denoted as An number, to classify the flow mode. An number is defined as the ratio of the time takes the parent artery flow transports across the IA neck to the time required for vortex ring formation. Based on the definition, the flow mode is vortex if An>1 and it is cavity if An<1. We show that the specific definition of Le et al. works for sidewall but needs to be modified for bifurcation aneurysms. In addition, we show that this classification works on three-dimensional geometries reconstructed from three-dimensional rotational angiography of human subjects. Furthermore, we verify the correlation of IA flow mode and WSS/OSI on the human subject IA. This work was supported partly by the NIH grant R03EB014860, and the computational resources were partly provided by CCR at UB. We thank Prof. Hui Meng and Dr. Jianping Xiang for providing us the database of aneurysms and helpful discussions.

  8. 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. PMID:25329326

  9. Classified and clustered data constellation: An efficient approach of 3D urban data management

    Science.gov (United States)

    Azri, Suhaibah; Ujang, Uznir; Castro, Francesc Antón; Rahman, Alias Abdul; Mioc, Darka

    2016-03-01

    The growth of urban areas has resulted in massive urban datasets and difficulties handling and managing issues related to urban areas. Huge and massive datasets can degrade data retrieval and information analysis performance. In addition, the urban environment is very difficult to manage because it involves various types of data, such as multiple types of zoning themes in the case of urban mixed-use development. Thus, a special technique for efficient handling and management of urban data is necessary. This paper proposes a structure called Classified and Clustered Data Constellation (CCDC) for urban data management. CCDC operates on the basis of two filters: classification and clustering. To boost up the performance of information retrieval, CCDC offers a minimal percentage of overlap among nodes and coverage area to avoid repetitive data entry and multipath query. The results of tests conducted on several urban mixed-use development datasets using CCDC verify that it efficiently retrieves their semantic and spatial information. Further, comparisons conducted between CCDC and existing clustering and data constellation techniques, from the aspect of preservation of minimal overlap and coverage, confirm that the proposed structure is capable of preserving the minimum overlap and coverage area among nodes. Our overall results indicate that CCDC is efficient in handling and managing urban data, especially urban mixed-use development applications.

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

    Directory of Open Access Journals (Sweden)

    Mohammad Hassan Moradi

    2012-08-01

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

  11. KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

    DEFF Research Database (Denmark)

    Pons, Tirso; Vazquez, Miguel; Matey-Hernandez, María Luisa;

    2016-01-01

    kinase variants in UniProt that remained unclassified. A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https......://github.com/Rbbt-Workflows/KinMut2. Conclusions: KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank...... as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families....

  12. Mindfulness for Students Classified with Emotional/Behavioral Disorder

    Science.gov (United States)

    Malow, Micheline S.; Austin, Vance L.

    2016-01-01

    A six-week investigation utilizing a standard mindfulness for adolescents curriculum and norm-based standardized resiliency scale was implemented in a self-contained school for students with Emotional/Behavioral Disorders (E/BD). Informal integration of mindfulness activities into a classroom setting was examined for ecological appropriateness and…

  13. Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

    Science.gov (United States)

    Anaya, Leticia H.

    2011-01-01

    In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

  14. China's Electronic Information Product Energy Consumption Standard

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    @@ The electronic information industry of China is facing increasingly urgent ecological challenges. This year, China will study and advance an electronic information product energy consumption standard, and establish a key list of pollution controls and classified frame system.

  15. See Change: Classifying single observation transients from HST using SNCosmo

    Science.gov (United States)

    Sofiatti Nunes, Caroline; Perlmutter, Saul; Nordin, Jakob; Rubin, David; Lidman, Chris; Deustua, Susana E.; Fruchter, Andrew S.; Aldering, Greg Scott; Brodwin, Mark; Cunha, Carlos E.; Eisenhardt, Peter R.; Gonzalez, Anthony H.; Jee, Myungkook J.; Hildebrandt, Hendrik; Hoekstra, Henk; Santos, Joana; Stanford, S. Adam; Stern, Dana R.; Fassbender, Rene; Richard, Johan; Rosati, Piero; Wechsler, Risa H.; Muzzin, Adam; Willis, Jon; Boehringer, Hans; Gladders, Michael; Goobar, Ariel; Amanullah, Rahman; Hook, Isobel; Huterer, Dragan; Huang, Jiasheng; Kim, Alex G.; Kowalski, Marek; Linder, Eric; Pain, Reynald; Saunders, Clare; Suzuki, Nao; Barbary, Kyle H.; Rykoff, Eli S.; Meyers, Joshua; Spadafora, Anthony L.; Hayden, Brian; Wilson, Gillian; Rozo, Eduardo; Hilton, Matt; Dixon, Samantha; Yen, Mike

    2016-01-01

    The Supernova Cosmology Project (SCP) is executing "See Change", a large HST program to look for possible variation in dark energy using supernovae at z>1. As part of the survey, we often must make time-critical follow-up decisions based on multicolor detection at a single epoch. We demonstrate the use of the SNCosmo software package to obtain simulated fluxes in the HST filters for type Ia and core-collapse supernovae at various redshifts. These simulations allow us to compare photometric data from HST with the distribution of the simulated SNe through methods such as Random Forest, a learning method for classification, and Gaussian Kernel Estimation. The results help us make informed decisions about triggered follow up using HST and ground based observatories to provide time-critical information needed about transients. Examples of this technique applied in the context of See Change are shown.

  16. ReqGIS Classifier: a tool for geographic requirements normalization

    OpenAIRE

    Saldaño, Viviana E.; Buccella, Agustina; Cechich, Alejandra

    2011-01-01

    Component Based Software Development (CBSD) is a development process based on components’ reuse. One of the main difficulties for developers is selecting the most suitable component that fit in their development systems. In this paper we describe a software tool, named ReqGIS, which supports our methodology for improving components’ identification in a geographic information environment. In particular, we introduce a new component named AlgSim, which completes the automation of the whole m...

  17. Spatial and Temporal knowledge representation techniques for traditional machine learning classifiers applied to remote sensing data.

    Science.gov (United States)

    Cervone, G.; Kafatos, M.

    2005-12-01

    Formulating general hypotheses from limited observations is one of the fundamental principles of scientific discovery. The data mining approach consists, among others, in generating new knowledge analyzing massive amounts of data and using background knowledge. Knowledge representation is one of the fundamental topics of data mining, because the representation language dictates which algorithms to use, as well as the effective usefulness of the learned hypotheses. Programs that use richer representation languages have the advantage of generating hypotheses that are compact and easy to understand, and the disadvantage of being more complex, slower and ususally with more control parameters. On the other hand, programs that use simpler representaiton languages overcome these shortcomings, but fail to generate hypotheses that can be easily interpreted and used for problem solving and decision making. Symbolic machine learning methods, such as decision rule classifiers, use a complex representation language which can be used to describe difficult concepts, and allow to cope with spatial and temporal data, such as remote sensing data. Because data are usually collected as a sequence of observations over time and in specific locations, very often it is necessary to find relations not only in the data per se, but also in the temporal and spatial distribution of the observations. Due to the increasingly large amount of spatial and temporal data collected and analyzed in several fields such as remote sensing, geographical information systems (GIS), bioinformatics, medicine, bank transactions, etc, spatial and temporal knowledge representaion has become a problem of crucial importance. Present research investigates methods to use existing symbolic machine learning classifiers with temporal and spatial data. The data are converted in a representation language which is suitable to learn spatial and temporal relationship without modifying the existing algorithms. Results from

  18. Informal Employment in Bangladesh

    OpenAIRE

    S. Maligalig, Dalisay; Cuevas, Sining; Rosario, Aleli

    2009-01-01

    The paper developed a methodology for classifying workers into formal and informal employment using the 2005 Bangladesh Labor Force Survey (LFS). Although the 2005 LFS was not designed to collect data for this purpose, it included questions that can be used to determine whether workers are engaged in formal or informal employment. However, the process of identifying the combination of questions that could distinguish between formal and informal workers was hampered by data inconsistencies tha...

  19. What about the regolith, the saprolite and the bedrock? Proposals for classifying the subsolum in WRB

    Science.gov (United States)

    Juilleret, Jérôme; Dondeyne, Stefaan; Hissler, Christophe

    2014-05-01

    Since soil surveys in the past were mainly conducted in support of agriculture, soil classification tended to focus on the solum representing mainly the upper part of the soil cover that is exploited by crops; the subsolum was largely neglected. When dealing with environmental issues - such as vegetation ecology, groundwater recharge, water quality or waste disposal - an integrated knowledge of the solum to subsolum continuum is required. In the World Reference Base for soil resources (WRB), the lower boundary for soil classification is set at 2 m, including both loose parent material as well as weathered and continuous rock. With the raised concern for environmental issues and global warming, classification concepts in WRB have been widened over the last decades. Cryosols were included as a separate Reference Soil Group to account for soils affected by perennial frost; Technosols were included to account for soils dominated by technical human activity. Terms for describing and classifying the subsolum are however still lacking. Nevertheless, during soil surveys a wealth of information on the subsolum is also collected. In Luxembourg, detailed soil surveys are conducted according to a national legend which is correlated to WRB. Quantitative data on characteristics of the subsolum, such as bedding, cleavage, fractures density and dipping of the layer, are recorded for their importance in relation to subsurface hydrology. Drawing from this experience, we propose defining four "subsolum materials" and which could be integrated into WRB as qualifiers. Regolitic materials are composed of soil and rock fragments deposited by water, solifluction, ice or wind; Paralithic materials consist of partly weathered rock with geogenic structural features; Saprolitic materials are formed from in situ weathering of the underlying geological deposits; Lithic materials correspond to unaltered bedrock. We discuss how these characteristics could be integrated into WRB and how additional

  20. REPTREE CLASSIFIER FOR IDENTIFYING LINK SPAM IN WEB SEARCH ENGINES

    OpenAIRE

    Jayanthi, S.K.; S.Sasikala

    2013-01-01

    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 ...

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

    CERN Document Server

    Yu, Jin; Zhang, Jian

    2012-01-01

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

  2. Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA

    Directory of Open Access Journals (Sweden)

    Yugal kumar

    2012-07-01

    Full Text Available In the field of Machine learning & Data Mining, lot of work had been done to construct new classification techniques/ classifiers and lot of research is going on to construct further new classifiers with the help of nature inspired technique such as Genetic Algorithm, Ant Colony Optimization, Bee Colony Optimization, Neural Network, Particle Swarm Optimization etc. Many researchers provided comparative study/ analysis of classification techniques. But this paper deals with another form of analysis of classification techniques i.e. parametric and non parametric classifiers analysis. This paper identifies parametric & non parametric classifiers that are used in classification process and provides tree representation of these classifiers. For the analysis purpose, four classifiers are used in which two of them are parametric and rest of are non-parametric in nature.

  3. 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.

  4. A consensus prognostic gene expression classifier for ER positive breast cancer

    OpenAIRE

    Teschendorff, Andrew E.; Naderi, Ali; Barbosa-Morais, Nuno L.; Pinder, Sarah E; Ellis, Ian O.; Aparicio, Sam; Brenton, James D.; Caldas, Carlos

    2006-01-01

    Background A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. Results Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the...

  5. Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

    Directory of Open Access Journals (Sweden)

    Aitor Álvarez

    2015-12-01

    Full Text Available In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking, is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one.

  6. Using Multivariate Machine Learning Methods and Structural MRI to Classify Childhood Onset Schizophrenia and Healthy Controls

    OpenAIRE

    DeannaGreenstein; JamesD.Malley

    2012-01-01

    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 posi...

  7. 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

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

    OpenAIRE

    Keith Jonathan M; Davey Christian M; Boyd Sarah E

    2012-01-01

    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 a...

  9. Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

    Science.gov (United States)

    Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley

    2006-01-01

    Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

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

    Institute of Scientific and Technical Information of China (English)

    张燕昆; 刘重庆

    2003-01-01

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

  11. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification

  12. Evaluation of the efficiency of biofield diagnostic system in breast cancer detection using clinical study results and classifiers.

    Science.gov (United States)

    Subbhuraam, Vinitha Sree; Ng, E Y K; Kaw, G; Acharya U, Rajendra; Chong, B K

    2012-02-01

    The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials using sensors. This technique is used by the Biofield Diagnostic System (BDS) to detect the presence of malignancy. This paper evaluates the efficiency of BDS in breast cancer detection and also evaluates the use of classifiers for improving the accuracy of BDS. 182 women scheduled for either mammography or ultrasound or both tests participated in the BDS clinical study conducted at Tan Tock Seng hospital, Singapore. Using the BDS index obtained from the BDS examination and the level of suspicion score obtained from mammography/ultrasound results, the final BDS result was deciphered. BDS demonstrated high values for sensitivity (96.23%), specificity (93.80%), and accuracy (94.51%). Also, we have studied the performance of five supervised learning based classifiers (back propagation network, probabilistic neural network, linear discriminant analysis, support vector machines, and a fuzzy classifier), by feeding selected features from the collected dataset. The clinical study results show that BDS can help physicians to differentiate benign and malignant breast lesions, and thereby, aid in making better biopsy recommendations. PMID:20703753

  13. Retrieval Architecture with Classified Query for Content Based Image Recognition

    Directory of Open Access Journals (Sweden)

    Rik Das

    2016-01-01

    Full Text Available The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

  14. Classifying Gamma-Ray Bursts with Gaussian Mixture Model

    CERN Document Server

    Yang, En-Bo; Choi, Chul-Sung; Chang, Heon-Young

    2016-01-01

    Using Gaussian Mixture Model (GMM) and Expectation Maximization Algorithm, we perform an analysis of time duration ($T_{90}$) for \\textit{CGRO}/BATSE, \\textit{Swift}/BAT and \\textit{Fermi}/GBM Gamma-Ray Bursts. The $T_{90}$ distributions of 298 redshift-known \\textit{Swift}/BAT GRBs have also been studied in both observer and rest frames. Bayesian Information Criterion has been used to compare between different GMM models. We find that two Gaussian components are better to describe the \\textit{CGRO}/BATSE and \\textit{Fermi}/GBM GRBs in the observer frame. Also, we caution that two groups are expected for the \\textit{Swift}/BAT bursts in the rest frame, which is consistent with some previous results. However, \\textit{Swift} GRBs in the observer frame seem to show a trimodal distribution, of which the superficial intermediate class may result from the selection effect of \\textit{Swift}/BAT.

  15. 32 CFR 2001.54 - Foreign government information.

    Science.gov (United States)

    2010-07-01

    ... safeguarding standards that may be necessary for foreign government information, other than NATO information... obligation. NATO classified information shall be safeguarded in compliance with USSAN 1-07. To the...

  16. A Combination of Off-line and On-line Learning to Classifier Grids for Object Detection

    Directory of Open Access Journals (Sweden)

    Nguyen Dang Binh

    2016-05-01

    Full Text Available We propose a new method for object detection by combining off-line and on-line boosting learning to classifier grids based on visual information without human intervention concerned to intelligent surveillance system. It allows for combine information labeled and unlabeled use different contexts to update the system, which is not available at off-line training time. The main goal is to develop an adaptive but robust system and to combine prior knowledge with new information in an unsupervised learning framework that is learning 24 hours a day and 7 days a week. We use co-training strategy by combining off-line and on-line learning to the classifier grids. The proposed method is practically favorable as it meets the requirements of real-time performance, accuracy and robustness. It works well with reasonable amount of training samples and is computational efficiency. Experiments on detection of objects in challenging data sets show the outperforming of our approach.

  17. Classifying machinery condition using oil samples and binary logistic regression

    Science.gov (United States)

    Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

    2015-08-01

    The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

  18. Neurodevelopmental Characteristics of Children with Learning Impairments Classified According to the Double-Deficit Hypothesis

    Science.gov (United States)

    Waber, Deborah P.; Forbes, Peter W.; Wolff, Peter H.; Weiler, Michael D.

    2004-01-01

    The double-deficit model has been examined primarily in relation to reading. We investigated whether children classified according to the double-deficit model would exhibit differences in other neuropsychological domains. Children referred for learning problems (N = 188), ages 7 to 11, were classified by double-deficit subtype. Only three of the…

  19. 32 CFR 989.26 - Classified actions (40 CFR 1507.3(c)).

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Classified actions (40 CFR 1507.3(c)). 989.26... ENVIRONMENTAL PROTECTION ENVIRONMENTAL IMPACT ANALYSIS PROCESS (EIAP) § 989.26 Classified actions (40 CFR 1507.3... security requirements. Where feasible, the EPF may need to help appropriate personnel from those...

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

    NARCIS (Netherlands)

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

    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