Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7740
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dc.contributor.authorXie, J-
dc.contributor.authorHone, K-
dc.contributor.authorXie, W-
dc.contributor.authorGao, X-
dc.contributor.authorShi, Y-
dc.contributor.authorLiu, X-
dc.date.accessioned2013-12-02T14:52:14Z-
dc.date.available2013-12-02T14:52:14Z-
dc.date.issued2013-
dc.identifier.citationIntelligent Data Analysis, 17(4), 649 - 664, 2013en_US
dc.identifier.issn1088-467X-
dc.identifier.urihttp://iospress.metapress.com/content/m031k153l8146546/?issue=4&genre=article&spage=649&issn=1088-467X&volume=17en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/7740-
dc.description© 2013 – IOS Press and the authors. All rights reserveden_US
dc.description.abstractTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.en_US
dc.description.sponsorshipThis work is supported in part by the grant of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026).en_US
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.subjectTwin support vector machinesen_US
dc.subjectMulticatigory data classificationen_US
dc.subjectMulticategory twin support machine classifiersen_US
dc.subjectSupport vector machinesen_US
dc.subjectPattern recognitionen_US
dc.subjectMachine learningen_US
dc.titleExtending twin support vector machine classifier for multi-category classification problemsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3233/IDA-130598-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute for Ageing Studies-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Intelligent Data Analysis-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/People and Interactivity Research Centre-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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