Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines
Identifieur interne : 000E31 ( Istex/Corpus ); précédent : 000E30; suivant : 000E32Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines
Auteurs : Zeki Erdem ; Robi Polikar ; Fikret Gurgen ; Nejat YumusakSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetting phenomenon. In our previous work, integrating the SVM classifiers into an ensemble framework using Learn++ (SVMLearn++) [1], we have shown that the SVM classifiers can in fact be equipped with the incremental learning capability. However, Learn++ suffers from an inherent out-voting problem: when asked to learn new classes, an unnecessarily large number of classifiers are generated to learn the new classes. In this paper, we propose a new ensemble based incremental learning approach using SVMs that is based on the incremental Learn++.MT algorithm. Experiments on the real-world and benchmark datasets show that the proposed approach can reduce the number of SVM classifiers generated, thus reduces the effect of out-voting problem. It also provides performance improvements over previous approach.
Url:
DOI: 10.1007/11550907_96
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ISTEX:7155B31BC88BB2678B987C67811112604D551617Le document en format XML
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It also provides performance improvements over previous approach.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Zeki Erdem</name><affiliations><json:string>Information Technologies Institute, TUBITAK Marmara Research Center, 41470, Gebze – Kocaeli, Turkey</json:string><json:string>Computer Engineering Department, Sakarya University, 54187, Esentepe, Sakarya, Turkey</json:string><json:string>E-mail: zeki.erdem@bte.mam.gov.tr</json:string></affiliations></json:item><json:item><name>Robi Polikar</name><affiliations><json:string>Electrical and Computer Engineering Department, Rowan University, 210 Mullica Hill Rd., 08028, Glassboro, NJ, USA</json:string><json:string>E-mail: polikar@rowan.edu</json:string></affiliations></json:item><json:item><name>Fikret Gurgen</name><affiliations><json:string>Computer Engineering Department, Bogazici University, 80815, Bebek, Istanbul, Turkey</json:string><json:string>E-mail: gurgen@boun.edu.tr</json:string></affiliations></json:item><json:item><name>Nejat Yumusak</name><affiliations><json:string>Computer Engineering Department, Sakarya University, 54187, Esentepe, Sakarya, Turkey</json:string><json:string>E-mail: nyumusak@sakarya.edu.tr</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetting phenomenon. 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ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-28756-8</BookID><BookTitle>Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005</BookTitle><BookSubTitle>15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II</BookSubTitle><BookVolumeNumber>3697</BookVolumeNumber><BookSequenceNumber>3697</BookSequenceNumber><BookDOI>10.1007/11550907</BookDOI><BookTitleID>126371</BookTitleID><BookPrintISBN>978-3-540-28755-1</BookPrintISBN><BookElectronicISBN>978-3-540-28756-8</BookElectronicISBN><BookChapterCount>163</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16013" Priority="1" Type="Secondary">Computation by Abstract Devices</BookSubject><BookSubject Code="I21017" Priority="2" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I2203X" Priority="3" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I18040" Priority="4" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18024" Priority="5" Type="Secondary">Database Management</BookSubject><BookSubject Code="B18006" Priority="6" Type="Secondary">Neurosciences</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Włodzisław</GivenName><FamilyName>Duch</FamilyName></EditorName><Contact><Email>wduch@is.umk.pl</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Janusz</GivenName><FamilyName>Kacprzyk</FamilyName></EditorName><Contact><Email>kacprzyk@ibspan.waw.pl</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Erkki</GivenName><FamilyName>Oja</FamilyName></EditorName><Contact><Email>Erkki.Oja@hut.fi</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Sławomir</GivenName><FamilyName>Zadrożny</FamilyName></EditorName><Contact><Email>zadrozny@ibspan.waw.pl</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Informatics</OrgDivision><OrgName>Nicolaus Copernicus University</OrgName><OrgAddress><City>Toruń</City><Country>Poland</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Systems Research Institute</OrgDivision><OrgName>Polish Academy of Sciences</OrgName><OrgAddress><Street>ul. Newelska 6</Street><Postcode>01–447</Postcode><City>Warsaw</City><Country>Poland</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Adaptive Informatics Research Centre</OrgDivision><OrgName>Helsinki University of Technology</OrgName><OrgAddress><Postbox>P.O. Box 5400</Postbox><Postcode>02015</Postcode><City>HUT</City><Country>Finland</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Systems Research Institute</OrgDivision><OrgName>Polish Academy of Sciences</OrgName><OrgAddress><Street>ul. Newelska 6</Street><Postcode>01-447</Postcode><City>Warsaw</City><Country>Poland</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part12"><PartInfo TocLevels="0"><PartID>12</PartID><PartSequenceNumber>12</PartSequenceNumber><PartTitle>Support Vector Machines and Kernel-Based Methods</PartTitle><PartChapterCount>14</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005</BookTitle></PartContext></PartInfo><Chapter ID="Chap96" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>96</ChapterID><ChapterDOI>10.1007/11550907_96</ChapterDOI><ChapterSequenceNumber>96</ChapterSequenceNumber><ChapterTitle Language="En">Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines</ChapterTitle><ChapterFirstPage>607</ChapterFirstPage><ChapterLastPage>612</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>12</PartID><BookID>978-3-540-28756-8</BookID><BookTitle>Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20 Aff23"><AuthorName DisplayOrder="Western"><GivenName>Zeki</GivenName><FamilyName>Erdem</FamilyName></AuthorName><Contact><Email>zeki.erdem@bte.mam.gov.tr</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Robi</GivenName><FamilyName>Polikar</FamilyName></AuthorName><Contact><Email>polikar@rowan.edu</Email></Contact></Author><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Fikret</GivenName><FamilyName>Gurgen</FamilyName></AuthorName><Contact><Email>gurgen@boun.edu.tr</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Nejat</GivenName><FamilyName>Yumusak</FamilyName></AuthorName><Contact><Email>nyumusak@sakarya.edu.tr</Email></Contact></Author><Affiliation ID="Aff20"><OrgDivision>Information Technologies Institute</OrgDivision><OrgName>TUBITAK Marmara Research Center</OrgName><OrgAddress><Postcode>41470</Postcode><City>Gebze – Kocaeli</City><Country>Turkey</Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgDivision>Electrical and Computer Engineering Department</OrgDivision><OrgName>Rowan University</OrgName><OrgAddress><Street>210 Mullica Hill Rd.</Street><City>Glassboro</City><State>NJ</State><Postcode>08028</Postcode><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgDivision>Computer Engineering Department</OrgDivision><OrgName>Bogazici University</OrgName><OrgAddress><City>Bebek</City><Postcode>80815</Postcode><State>Istanbul</State><Country>Turkey</Country></OrgAddress></Affiliation><Affiliation ID="Aff23"><OrgDivision>Computer Engineering Department</OrgDivision><OrgName>Sakarya University</OrgName><OrgAddress><City>Esentepe</City><Postcode>54187</Postcode><State>Sakarya</State><Country>Turkey</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the <Emphasis Type="Italic">catastrophic forgetting</Emphasis> phenomenon. In our previous work, integrating the SVM classifiers into an ensemble framework using Learn++ (SVMLearn++) [1], we have shown that the SVM classifiers can in fact be equipped with the incremental learning capability. However, Learn++ suffers from an inherent <Emphasis Type="Italic">out-voting</Emphasis> problem: when asked to learn new classes, an unnecessarily large number of classifiers are generated to learn the new classes. In this paper, we propose a new ensemble based incremental learning approach using SVMs that is based on the incremental Learn++.MT algorithm. Experiments on the real-world and benchmark datasets show that the proposed approach can reduce the number of SVM classifiers generated, thus reduces the effect of <Emphasis Type="Italic">out-voting</Emphasis> problem. It also provides performance improvements over previous approach.</Para></Abstract><ArticleNote Type="Misc"><SimplePara>An erratum to this chapter can be found at <ExternalRef><RefSource><Literal>http://dx.doi.org/10.1007/11550907_163</Literal></RefSource><RefTarget Address="http://dx.doi.org/10.1007/11550907_163" TargetType="URL"></RefTarget></ExternalRef>.</SimplePara></ArticleNote></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines</title></titleInfo><name type="personal"><namePart type="given">Zeki</namePart><namePart type="family">Erdem</namePart><affiliation>Information Technologies Institute, TUBITAK Marmara Research Center, 41470, Gebze – Kocaeli, Turkey</affiliation><affiliation>Computer Engineering Department, Sakarya University, 54187, Esentepe, Sakarya, Turkey</affiliation><affiliation>E-mail: zeki.erdem@bte.mam.gov.tr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Robi</namePart><namePart type="family">Polikar</namePart><affiliation>Electrical and Computer Engineering Department, Rowan University, 210 Mullica Hill Rd., 08028, Glassboro, NJ, USA</affiliation><affiliation>E-mail: polikar@rowan.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Fikret</namePart><namePart type="family">Gurgen</namePart><affiliation>Computer Engineering Department, Bogazici University, 80815, Bebek, Istanbul, Turkey</affiliation><affiliation>E-mail: gurgen@boun.edu.tr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Nejat</namePart><namePart type="family">Yumusak</namePart><affiliation>Computer Engineering Department, Sakarya University, 54187, Esentepe, Sakarya, Turkey</affiliation><affiliation>E-mail: nyumusak@sakarya.edu.tr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetting phenomenon. In our previous work, integrating the SVM classifiers into an ensemble framework using Learn++ (SVMLearn++) [1], we have shown that the SVM classifiers can in fact be equipped with the incremental learning capability. However, Learn++ suffers from an inherent out-voting problem: when asked to learn new classes, an unnecessarily large number of classifiers are generated to learn the new classes. In this paper, we propose a new ensemble based incremental learning approach using SVMs that is based on the incremental Learn++.MT algorithm. Experiments on the real-world and benchmark datasets show that the proposed approach can reduce the number of SVM classifiers generated, thus reduces the effect of out-voting problem. It also provides performance improvements over previous approach.</abstract><relatedItem type="otherVersion"><titleInfo><title>An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .</title></titleInfo><identifier type="doi">10.1007/11550907_163</identifier><identifier type="URL">http://dx.doi.org/10.1007/11550907_163</identifier></relatedItem><relatedItem type="host"><titleInfo><title>Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005</title><subTitle>15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II</subTitle></titleInfo><name type="personal"><namePart type="given">Włodzisław</namePart><namePart type="family">Duch</namePart><affiliation>Department of Informatics, Nicolaus Copernicus University, Toruń, Poland</affiliation><affiliation>E-mail: wduch@is.umk.pl</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Janusz</namePart><namePart type="family">Kacprzyk</namePart><affiliation>Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland</affiliation><affiliation>E-mail: kacprzyk@ibspan.waw.pl</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Erkki</namePart><namePart type="family">Oja</namePart><affiliation>Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland</affiliation><affiliation>E-mail: Erkki.Oja@hut.fi</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Sławomir</namePart><namePart type="family">Zadrożny</namePart><affiliation>Systems Research Institute, Polish Academy of Sciences, ul. 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