untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:42:46Z</responseDate>
<request identifier=oai:HAL:hal-00602260v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-00602260v1</identifier>
<datestamp>2018-01-11</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:info</setSpec>
<setSpec>collection:CNRS</setSpec>
<setSpec>collection:XLIM</setSpec>
<setSpec>collection:UNILIM</setSpec>
<setSpec>collection:XLIM-SIC</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:BNRMI</setSpec>
<setSpec>collection:UNIV-POITIERS</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=fr>Sélection des variables optimales par optimisation multi-objective de l'information mutuelle</title>
<creator>Grandchamp, Enguerran</creator>
<creator>Alata, Olivier</creator>
<creator>Olivier, Christian</creator>
<creator>Khoudeir, Majdi</creator>
<creator>Abadi, Mohamed</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>SIC ; XLIM (XLIM) ; Université de Limoges (UNILIM) - Centre National de la Recherche Scientifique (CNRS) - Université de Limoges (UNILIM) - Centre National de la Recherche Scientifique (CNRS) - Université de Poitiers</contributor>
<description>International audience</description>
<source>GRETSI Proceedings</source>
<source>GRETSI</source>
<coverage>Bordeaux, France</coverage>
<identifier>hal-00602260</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00602260</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00602260</source>
<source>GRETSI, Sep 2011, Bordeaux, France. pp.1, 2011</source>
<language>fr</language>
<subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This work proposes an original approach using mutual information and Pareto curve jointly for feature selection. Mutual information is used to estimate dependency criterion between features and classes and redundancy criterion between features taken two by two. Unlike some studies, these criteria are used simultaneously to compute Pareto curve and determine the optimal feature sets. This approach is tested on more reference data. Several clustering algorithms are used to compute classification accuracy. The obtained results show the importance of our tools and its ability to select the best feature sets that give the better description.</description>
<date>2011-09-01</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
