Optimisation multi-objectif pour la sélection de modèles SVM
Identifieur interne : 000683 ( Hal/Corpus ); précédent : 000682; suivant : 000684Optimisation multi-objectif pour la sélection de modèles SVM
Auteurs : Clément Chatelain ; Yannick Oufella ; Sébastien Adam ; Yves Lecourtier ; Laurent HeutteSource :
Abstract
Dans cet article, nous proposons une méthode d'optimisation multi-objectif pour la sélection de modèle SVM, en utilisant l'algorithme NSGA-II. Le faux rejet et la fausse acceptation sont les deux critères employés pour trouver les hyperparameters optimaux d'un ensemble de classifieurs SVM. La stratégie proposée est appliquée à une tâche de discrimination chiffre/rejet embarquée dans un système plus global d'extraction d'information dans des documents manuscrits. Nos expérimentations sur une base réelle de chiffres/rejet montrent que notre méthode fournit de meilleurs résultats que les techniques d'optimisation mono-objectif plus classiques telle que l'apprentissage basé sour un critère d'aire sous la courbe ROC. Ces résultats sont dans un second temps validés sur des bases de l'UCI.
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Hal:hal-00671129Le document en format XML
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76821 Mont-Saint-Aignan</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-rouen.fr/</ref></desc></org></tutelle><tutelle active="#struct-301288" type="indirect"><org type="department" xml:id="struct-301288" status="VALID"><orgName>Institut National des Sciences Appliquées - Rouen</orgName><orgName type="acronym">INSA Rouen</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-301232" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-301232" type="indirect"><org type="institution" xml:id="struct-301232" status="VALID"><orgName>Institut National des Sciences Appliquées</orgName><orgName type="acronym">INSA</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation></affiliation></author><author><name sortKey="Heutte, Laurent" sort="Heutte, Laurent" uniqKey="Heutte L" first="Laurent" last="Heutte">Laurent Heutte</name><affiliation><hal:affiliation type="researchteam" xml:id="struct-389520" status="INCOMING"> <orgName>DOCAPP</orgName> <desc> <address> <country key="FR"></country> </address> </desc> <listRelation> <relation active="#struct-23832" type="direct"></relation> <relation active="#struct-300317" type="indirect"></relation> <relation name="EA4108" active="#struct-300318" type="indirect"></relation> <relation active="#struct-301288" type="indirect"></relation> <relation active="#struct-301232" type="indirect"></relation> </listRelation><tutelles><tutelle active="#struct-23832" type="direct"><org type="laboratory" xml:id="struct-23832" status="VALID"><orgName>Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</orgName><orgName type="acronym">LITIS</orgName><desc><address><addrLine>Avenue de l'Université UFR des Sciences et Techniques 76800 Saint-Etienne du Rouvray</addrLine><country key="FR"></country></address><ref type="url">http://www.litislab.eu</ref></desc><listRelation><relation active="#struct-300317" type="direct"></relation><relation name="EA4108" active="#struct-300318" type="direct"></relation><relation active="#struct-301288" type="direct"></relation><relation active="#struct-301232" type="indirect"></relation></listRelation></org></tutelle><tutelle active="#struct-300317" type="indirect"><org type="institution" xml:id="struct-300317" status="VALID"><orgName>Université du Havre</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle name="EA4108" active="#struct-300318" type="indirect"><org type="institution" xml:id="struct-300318" status="VALID"><orgName>Université de Rouen</orgName><desc><address><addrLine> 1 rue Thomas Becket - 76821 Mont-Saint-Aignan</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-rouen.fr/</ref></desc></org></tutelle><tutelle active="#struct-301288" type="indirect"><org type="department" xml:id="struct-301288" status="VALID"><orgName>Institut National des Sciences Appliquées - Rouen</orgName><orgName type="acronym">INSA Rouen</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-301232" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-301232" type="indirect"><org type="institution" xml:id="struct-301232" status="VALID"><orgName>Institut National des Sciences Appliquées</orgName><orgName type="acronym">INSA</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Dans cet article, nous proposons une méthode d'optimisation multi-objectif pour la sélection de modèle SVM, en utilisant l'algorithme NSGA-II. Le faux rejet et la fausse acceptation sont les deux critères employés pour trouver les hyperparameters optimaux d'un ensemble de classifieurs SVM. La stratégie proposée est appliquée à une tâche de discrimination chiffre/rejet embarquée dans un système plus global d'extraction d'information dans des documents manuscrits. Nos expérimentations sur une base réelle de chiffres/rejet montrent que notre méthode fournit de meilleurs résultats que les techniques d'optimisation mono-objectif plus classiques telle que l'apprentissage basé sour un critère d'aire sous la courbe ROC. Ces résultats sont dans un second temps validés sur des bases de l'UCI.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Optimisation multi-objectif pour la sélection de modèles SVM</title><author role="aut"><persName><forename type="first">Clément</forename><surname>Chatelain</surname></persName><email></email><idno type="halauthor">449874</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Yannick</forename><surname>Oufella</surname></persName><email></email><idno type="halauthor">694136</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Sébastien</forename><surname>Adam</surname></persName><email></email><idno type="halauthor">86892</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Yves</forename><surname>Lecourtier</surname></persName><email></email><idno type="halauthor">167932</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Heutte</surname></persName><email></email><idno type="halauthor">86899</idno><affiliation ref="#struct-389520"></affiliation></author><editor role="depositor"><persName><forename>Sébastien</forename><surname>Adam</surname></persName><email>Sebastien.Adam@univ-rouen.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2012-02-17 10:32:43</date><date type="whenModified">2015-05-19 16:34:42</date><date type="whenReleased">2012-02-17 11:02:09</date><date type="whenProduced">2008-01-22</date><date type="whenEndEmbargoed">2012-02-17</date><ref type="file" target="https://hal.archives-ouvertes.fr/hal-00671129/document"><date notBefore="2012-02-17"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.archives-ouvertes.fr/hal-00671129/file/rfia2008.pdf"><date notBefore="2012-02-17"></date></ref></edition><respStmt><resp>contributor</resp><name key="131493"><persName><forename>Sébastien</forename><surname>Adam</surname></persName><email>Sebastien.Adam@univ-rouen.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00671129</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-00671129</idno><idno type="halBibtex">chatelain:hal-00671129</idno><idno type="halRefHtml">Reconnaissance des Formes et Intelligence Artificielle (RFIA'08), Jan 2008, France. pp.67-72, 2008</idno><idno type="halRef">Reconnaissance des Formes et Intelligence Artificielle (RFIA'08), Jan 2008, France. pp.67-72, 2008</idno></publicationStmt><seriesStmt><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="UNIV-ROUEN">Université de Rouen</idno><idno type="stamp" n="LITIS">Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</idno><idno type="stamp" n="COMUE-NORMANDIE">Normandie Université</idno></seriesStmt><notesStmt><note type="audience" n="3">National</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Optimisation multi-objectif pour la sélection de modèles SVM</title><author role="aut"><persName><forename type="first">Clément</forename><surname>Chatelain</surname></persName><idno type="halAuthorId">449874</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Yannick</forename><surname>Oufella</surname></persName><idno type="halAuthorId">694136</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Sébastien</forename><surname>Adam</surname></persName><idno type="halAuthorId">86892</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Yves</forename><surname>Lecourtier</surname></persName><idno type="halAuthorId">167932</idno><affiliation ref="#struct-389520"></affiliation></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Heutte</surname></persName><idno type="halAuthorId">86899</idno><affiliation ref="#struct-389520"></affiliation></author></analytic><monogr><title level="m">Actes du congrès Reconnaissance des Formes et Intelligence Artificielle (RFIA'08)</title><meeting><title>Reconnaissance des Formes et Intelligence Artificielle (RFIA'08)</title><date type="start">2008-01-22</date><date type="end">2008-01-26</date><country key="FR">France</country></meeting><imprint><biblScope unit="pp">67-72</biblScope><date type="datePub">2008-01-22</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="fr">Dans cet article, nous proposons une méthode d'optimisation multi-objectif pour la sélection de modèle SVM, en utilisant l'algorithme NSGA-II. Le faux rejet et la fausse acceptation sont les deux critères employés pour trouver les hyperparameters optimaux d'un ensemble de classifieurs SVM. La stratégie proposée est appliquée à une tâche de discrimination chiffre/rejet embarquée dans un système plus global d'extraction d'information dans des documents manuscrits. Nos expérimentations sur une base réelle de chiffres/rejet montrent que notre méthode fournit de meilleurs résultats que les techniques d'optimisation mono-objectif plus classiques telle que l'apprentissage basé sour un critère d'aire sous la courbe ROC. Ces résultats sont dans un second temps validés sur des bases de l'UCI.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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