The Multilevel Classification Problem and a Monotonicity Hint
Identifieur interne : 000673 ( Main/Corpus ); précédent : 000672; suivant : 000674The Multilevel Classification Problem and a Monotonicity Hint
Auteurs : Malik Magdon-Ismail ; Justin Chen ; S. Abu-MostafaSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: We introduce and formalize the multilevel classification problem, in which each category can be subdivided into different levels. We analyze the framework in a Bayesian setting using Normal class conditional densities. Within this framework, a natural monotonicity hint converts the problem into a nonlinear programming task, with non-linear constraints. We present Monte Carlo and gradient based techniques for addressing this task, and show the results of simulations. Incorporation of monotonicity yields a systematic improvement in performance.
Url:
DOI: 10.1007/3-540-45675-9_61
Links to Exploration step
ISTEX:376201D87651F8AF9266A5E7138177D15F2A8E9ALe document en format XML
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Problem and a Monotonicity Hint</ChapterTitle><ChapterFirstPage>410</ChapterFirstPage><ChapterLastPage>415</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2002</CopyrightYear></ChapterCopyright><ChapterHistory><RegistrationDate><Year>2002</Year><Month>8</Month><Day>19</Day></RegistrationDate><OnlineDate><Year>2002</Year><Month>8</Month><Day>20</Day></OnlineDate></ChapterHistory><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>7</PartID><BookID>3-540-45675-9</BookID><BookTitle>Intelligent Data Engineering and Automated Learning — IDEAL 2002</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff7"><AuthorName DisplayOrder="Western"><GivenName>Malik</GivenName><FamilyName>Magdon-Ismail</FamilyName></AuthorName><Contact><Email>magdon@rpi.edu</Email></Contact></Author><Author AffiliationIDS="Aff7"><AuthorName DisplayOrder="Western"><GivenName>Hung-Ching</GivenName><GivenName>(Justin)</GivenName><FamilyName>Chen</FamilyName></AuthorName><Contact><Email>chenh3@rpi.edu</Email></Contact></Author><Author AffiliationIDS="Aff8"><AuthorName DisplayOrder="Western"><GivenName>Yaser</GivenName><GivenName>S.</GivenName><FamilyName>Abu-Mostafa</FamilyName></AuthorName><Contact><Email>yaser@cs.caltech.edu</Email></Contact></Author><Affiliation ID="Aff7"><OrgName>Dept. of Computer Science, RPI</OrgName><OrgAddress><Street>Lally 207, 110 8th Street</Street><City>Troy</City><State>NY</State><Country>USA</Country><Postcode>12180</Postcode></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>Learning Systems Group</OrgName><OrgAddress><Street>136-93, Caltech</Street><City>Pasadena</City><State>CA</State><Country>USA</Country><Postcode>91125</Postcode></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We introduce and formalize the multilevel classification problem, in which each category can be subdivided into different levels. We analyze the framework in a Bayesian setting using Normal class conditional densities. Within this framework, a natural monotonicity hint converts the problem into a nonlinear programming task, with non-linear constraints. We present Monte Carlo and gradient based techniques for addressing this task, and show the results of simulations. Incorporation of monotonicity yields a systematic improvement in performance.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>The Multilevel Classification Problem and a Monotonicity Hint</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>The Multilevel Classification Problem and a Monotonicity Hint</title></titleInfo><name type="personal"><namePart type="given">Malik</namePart><namePart type="family">Magdon-Ismail</namePart><affiliation>Dept. of Computer Science, RPI, Lally 207, 110 8th Street, 12180, Troy, NY, USA</affiliation><affiliation>E-mail: magdon@rpi.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Hung-Ching</namePart><namePart type="given">(Justin)</namePart><namePart type="family">Chen</namePart><affiliation>Dept. of Computer Science, RPI, Lally 207, 110 8th Street, 12180, Troy, NY, USA</affiliation><affiliation>E-mail: chenh3@rpi.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yaser</namePart><namePart type="given">S.</namePart><namePart type="family">Abu-Mostafa</namePart><affiliation>Learning Systems Group, 136-93, Caltech, 91125, Pasadena, CA, USA</affiliation><affiliation>E-mail: yaser@cs.caltech.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2002-08-20</dateIssued><copyrightDate encoding="w3cdtf">2002</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: We introduce and formalize the multilevel classification problem, in which each category can be subdivided into different levels. We analyze the framework in a Bayesian setting using Normal class conditional densities. Within this framework, a natural monotonicity hint converts the problem into a nonlinear programming task, with non-linear constraints. We present Monte Carlo and gradient based techniques for addressing this task, and show the results of simulations. Incorporation of monotonicity yields a systematic improvement in performance.</abstract><relatedItem type="host"><titleInfo><title>Intelligent Data Engineering and Automated Learning — IDEAL 2002</title><subTitle>Third International Conference Manchester, UK, August 12–14, 2002 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Hujun</namePart><namePart type="family">Yin</namePart><affiliation>Department of Electrical Engineering and Electronics, UMIST, M60 1QD, Manchester, UK</affiliation><affiliation>E-mail: h.yin@umist.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Nigel</namePart><namePart type="family">Allinson</namePart><affiliation>Department of Electrical Engineering and Electronics, UMIST, M60 1QD, Manchester, UK</affiliation><affiliation>E-mail: allinson@umist.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Richard</namePart><namePart type="family">Freeman</namePart><affiliation>Department of Electrical Engineering and Electronics, UMIST, M60 1QD, Manchester, UK</affiliation><affiliation>E-mail: rics@swift.ee.umist.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="family">Keane</namePart><affiliation>Department of Computation, UMIST, M60 1QD, Manchester, UK</affiliation><affiliation>E-mail: jak@co.umist.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Simon</namePart><namePart type="family">Hubbard</namePart><affiliation>Department of Biomolecular Science, UMIST, M60 1QD, Manchester, UK</affiliation><affiliation>E-mail: sjh@bms.umist.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2002</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="L15001">Bioinformatics</topic></subject><identifier type="DOI">10.1007/3-540-45675-9</identifier><identifier type="ISBN">978-3-540-44025-3</identifier><identifier type="eISBN">978-3-540-45675-9</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="BookTitleID">72670</identifier><identifier type="BookID">3-540-45675-9</identifier><identifier type="BookChapterCount">89</identifier><identifier type="BookVolumeNumber">2412</identifier><identifier type="BookSequenceNumber">2412</identifier><identifier type="PartChapterCount">11</identifier><part><date>2002</date><detail type="part"><title>Financial Engineering</title></detail><detail type="volume"><number>2412</number><caption>vol.</caption></detail><extent unit="pages"><start>410</start><end>415</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2002</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Goos</namePart><affiliation>Karlsruhe University, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Juris</namePart><namePart type="family">Hartmanis</namePart><affiliation>Cornell University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jan</namePart><namePart type="family">van Leeuwen</namePart><affiliation>Utrecht University, The Netherlands</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2002</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2002</recordOrigin></recordInfo></relatedItem><identifier type="istex">376201D87651F8AF9266A5E7138177D15F2A8E9A</identifier><identifier type="DOI">10.1007/3-540-45675-9_61</identifier><identifier type="ChapterID">61</identifier><identifier type="ChapterID">Chap61</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2002</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2002</recordOrigin></recordInfo></mods></metadata><enrichments><json:item><type>multicat</type><uri>https://api.istex.fr/document/376201D87651F8AF9266A5E7138177D15F2A8E9A/enrichments/multicat</uri></json:item><istex:refBibTEI uri="https://api.istex.fr/document/376201D87651F8AF9266A5E7138177D15F2A8E9A/enrichments/refBib"><teiHeader></teiHeader><text><front></front><body></body><back><listBibl><biblStruct xml:id="b0"><analytic><title></title><author><persName><forename type="first">Y</forename><surname>Abu-Mostafa</surname></persName></author></analytic><monogr><title level="j">Hints. 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