Classification active de flux de documents avec identification des nouvelles classes
Identifieur interne : 000B85 ( Hal/Checkpoint ); précédent : 000B84; suivant : 000B86Classification active de flux de documents avec identification des nouvelles classes
Auteurs : Mohamed-Rafik Bouguelia [France] ; Yolande Belaïd [France] ; Abdel Belaïd [France]Source :
Abstract
In this paper, we propose a stream-based semi-supervised active learning method for document classification, which is able to query (from an operator) the class labels of documents that are informative, according to an uncertainty measure. The method maintains a dynamically evolving graph topology of labelled document-representatives, which constitutes a covered feature space. The method is able to automatically discover the emergence of novel classes in the stream. An incoming document is identified as a member of a novel class or an existing class, depending on whether it is outside or inside the area covered by the known classes. Experiments on different real datasets show that the proposed method requires a small amount of the incoming documents to be labelled, in order to learn a model which achieves better or equal accuracy than to the usual supervised methods with fully labelled training documents.
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Hal:hal-00980698Le document en format XML
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="it"><term>Classification</term><term>Document stream</term><term>Novel class identification</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we propose a stream-based semi-supervised active learning method for document classification, which is able to query (from an operator) the class labels of documents that are informative, according to an uncertainty measure. The method maintains a dynamically evolving graph topology of labelled document-representatives, which constitutes a covered feature space. The method is able to automatically discover the emergence of novel classes in the stream. An incoming document is identified as a member of a novel class or an existing class, depending on whether it is outside or inside the area covered by the known classes. Experiments on different real datasets show that the proposed method requires a small amount of the incoming documents to be labelled, in order to learn a model which achieves better or equal accuracy than to the usual supervised methods with fully labelled training documents.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Classification active de flux de documents avec identification des nouvelles classes</title><author role="aut"><persName><forename type="first">Mohamed-Rafik</forename><surname>Bouguelia</surname></persName><email>mohamed.bouguelia@loria.fr</email><idno type="halauthor">881315</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-206042"></affiliation></author><author role="aut"><persName><forename type="first">Yolande</forename><surname>Belaïd</surname></persName><email>Yolande.Belaid@loria.fr</email><idno type="idhal">yolande-belaid</idno><idno type="halauthor">891817</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-206042"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><email>Abdel.Belaid@loria.fr</email><idno type="halauthor">808329</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-206042"></affiliation></author><editor role="depositor"><persName><forename>Yolande</forename><surname>Belaid</surname></persName><email>yolande.belaid@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-04-18 15:54:20</date><date type="whenModified">2015-09-21 11:19:38</date><date type="whenReleased">2014-04-18 15:58:20</date><date type="whenProduced">2014-03-19</date><date type="whenEndEmbargoed">2014-04-18</date><ref type="file" target="https://hal.inria.fr/hal-00980698/document"><date notBefore="2014-04-18"></date></ref><ref type="file" subtype="publisherAgreement" n="1" target="https://hal.inria.fr/hal-00980698/file/CIFED_version_editeur.pdf"><date notBefore="2014-04-18"></date></ref></edition><respStmt><resp>contributor</resp><name key="116035"><persName><forename>Yolande</forename><surname>Belaid</surname></persName><email>yolande.belaid@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00980698</idno><idno type="halUri">https://hal.inria.fr/hal-00980698</idno><idno type="halBibtex">bouguelia:hal-00980698</idno><idno type="halRefHtml">CIFED - Colloque International Francophone sur l'Écrit et le Document, Mar 2014, Nancy, France. pp.75-89, 2014</idno><idno type="halRef">CIFED - Colloque International Francophone sur l'Écrit et le Document, Mar 2014, Nancy, France. pp.75-89, 2014</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</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">Classification active de flux de documents avec identification des nouvelles classes</title><author role="aut"><persName><forename type="first">Mohamed-Rafik</forename><surname>Bouguelia</surname></persName><email>mohamed.bouguelia@loria.fr</email><idno type="halAuthorId">881315</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-206042"></affiliation></author><author role="aut"><persName><forename type="first">Yolande</forename><surname>Belaïd</surname></persName><email>Yolande.Belaid@loria.fr</email><idno type="idHal">yolande-belaid</idno><idno type="halAuthorId">891817</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-206042"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><email>Abdel.Belaid@loria.fr</email><idno type="halAuthorId">808329</idno><orgName ref="#struct-413289"></orgName><affiliation ref="#struct-206042"></affiliation></author></analytic><monogr><meeting><title>CIFED - Colloque International Francophone sur l'Écrit et le Document</title><date type="start">2014-03-19</date><date type="end">2014-03-21</date><settlement>Nancy</settlement><country key="FR">France</country></meeting><imprint><biblScope unit="pp">75-89</biblScope><date type="datePub">2014-03-19</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="it">Document stream</term><term xml:lang="it">Classification</term><term xml:lang="it">Novel class identification</term></keywords><classCode scheme="halDomain" n="info.info-hc">Computer Science [cs]/Human-Computer Interaction [cs.HC]</classCode><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">In this paper, we propose a stream-based semi-supervised active learning method for document classification, which is able to query (from an operator) the class labels of documents that are informative, according to an uncertainty measure. The method maintains a dynamically evolving graph topology of labelled document-representatives, which constitutes a covered feature space. The method is able to automatically discover the emergence of novel classes in the stream. An incoming document is identified as a member of a novel class or an existing class, depending on whether it is outside or inside the area covered by the known classes. Experiments on different real datasets show that the proposed method requires a small amount of the incoming documents to be labelled, in order to learn a model which achieves better or equal accuracy than to the usual supervised methods with fully labelled training documents.</abstract><abstract xml:lang="fr">Dans cet article, on propose un algorithme semi-supervisé actif pour la classification de flux continu de documents. Cet algorithme, basé sur une méthode adaptative d'apprentissage non supervisé, permet de repérer les documents les plus informatifs à l'aide d'une mesure d'incertitude pour demander leur étiquette à un opérateur. Il construit et maintient un modèle sous forme d'un graphe à topologie dynamique dont les nœuds sont des représentants de documents étiquetés, formant ce qu'on appelle l'"espace couvert par les classes connues". Il permet de détecter automatiquement les nouvelles classes apparaissant dans le flux. Un document est identifié comme membre d'une nouvelle classe ou d'une classe connue, selon qu'il se trouve à l'extérieur ou à l'intérieur de l'espace couvert par les classes connues. Les expérimentations effectuées sur des ensembles de documents réels montrent que la méthode nécessite peu de documents à étiqueter et qu'elle atteint des performances comparables aux méthodes supervisées qui sont entraînées sur des ensembles de documents présents en mémoire et entièrement étiquetés.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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