Improving Naive Bayes Using Class-Conditional ICA
Identifieur interne : 001823 ( Main/Exploration ); précédent : 001822; suivant : 001824Improving Naive Bayes Using Class-Conditional ICA
Auteurs : Marco Bressan [Espagne] ; Jordi Vitrià [Espagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: In the past years, Naive Bayes has experienced a renaissance in machine learning, particularly in the area of information retrieval. This classifier is based on the not always realistic assumption that class-conditional distributions can be factorized in the product of their marginal densities. On the other side, one of the most common ways of estimating the Independent Component Analysis (ICA) representation for a given random vector consists in minimizing the Kullback-Leibler distance between the joint density and the product of the marginal densities (mutual information). From this that ICA provides a representation where the independence assumption can be held on stronger grounds. In this paper we propose class-conditional ICA as a method that provides an adequate representation where Naive Bayes is the classifier of choice. Experiments on two public databases are performed in order to confirm this hypothesis.
Url:
DOI: 10.1007/3-540-36131-6_1
Affiliations:
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Informàtica, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName><orgName type="university">Université autonome de Barcelone</orgName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Espagne</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2002</date></imprint><idno type="ISSN">0302-9743</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">2AD5A8AAE80DBD3AFE7AB4372FE81C307FF76A8B</idno><idno type="DOI">10.1007/3-540-36131-6_1</idno><idno type="ChapterID">1</idno><idno type="ChapterID">Chap1</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: In the past years, Naive Bayes has experienced a renaissance in machine learning, particularly in the area of information retrieval. This classifier is based on the not always realistic assumption that class-conditional distributions can be factorized in the product of their marginal densities. On the other side, one of the most common ways of estimating the Independent Component Analysis (ICA) representation for a given random vector consists in minimizing the Kullback-Leibler distance between the joint density and the product of the marginal densities (mutual information). From this that ICA provides a representation where the independence assumption can be held on stronger grounds. In this paper we propose class-conditional ICA as a method that provides an adequate representation where Naive Bayes is the classifier of choice. Experiments on two public databases are performed in order to confirm this hypothesis.</div></front></TEI><affiliations><list><country><li>Espagne</li></country><region><li>Catalogne</li></region><settlement><li>Barcelone</li></settlement><orgName><li>Université autonome de Barcelone</li></orgName></list><tree><country name="Espagne"><region name="Catalogne"><name sortKey="Bressan, Marco" sort="Bressan, Marco" uniqKey="Bressan M" first="Marco" last="Bressan">Marco Bressan</name></region><name sortKey="Bressan, Marco" sort="Bressan, Marco" uniqKey="Bressan M" first="Marco" last="Bressan">Marco Bressan</name><name sortKey="Vitria, Jordi" sort="Vitria, Jordi" uniqKey="Vitria J" first="Jordi" last="Vitrià">Jordi Vitrià</name><name sortKey="Vitria, Jordi" sort="Vitria, Jordi" uniqKey="Vitria J" first="Jordi" last="Vitrià">Jordi Vitrià</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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