Approaches to feature selection for document categorization

dc.contributor.author	Kou, Huaizhong
dc.contributor.author	Gardarin, Georges
dc.contributor.author	Zeitouni, Karina
dc.contributor.editor	Düsterhöft, Antje
dc.contributor.editor	Thalheim, Bernhard
dc.date.accessioned	2019-11-14T11:21:40Z
dc.date.available	2019-11-14T11:21:40Z
dc.date.issued	2003
dc.description.abstract	One of the problems faced by document categorization is that terms present in the collection of example documents are numerous. From the point of view of coherence between the models used in document categorization, we analyses the frameworks of both k-NN and NB categorization models and feature selection problem. Two algorithms CBA and IBA to feature selection are proposed. The empirical results done with k-NN and NB classifiers show that the coherence between models in the categorization system can bring benefits for performance.	en
dc.identifier.isbn	3-88579-358-X
dc.identifier.pissn	1617-5468
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/29882
dc.language.iso	en
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	Natural language processing and information systems
dc.relation.ispartofseries	Lecture Notes in Informatics (LNI) - Proceedings, Volume P-29
dc.title	Approaches to feature selection for document categorization	en
dc.type	Text/Conference Paper
gi.citation.endPage	154
gi.citation.publisherPlace	Bonn
gi.citation.startPage	141
gi.conference.date	June 2003
gi.conference.location	Burg (Spreewald)
gi.conference.sessiontitle	Regular Research Papers

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