A generalization of independence in naive bayes model

Yu Fujimoto*, Noboru Murata

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this paper, generalized statistical independence is proposed from the viewpoint of generalized multiplication characterized by a monotonically increasing function and its inverse function, and it is implemented in naive Bayes models. This paper also proposes an idea of their estimation method which directly uses empirical marginal distributions to retain simplicity of calculation. Our method is interpreted as an optimization of a rough approximation of the Bregman divergence so that it is expected to have a kind of robust property. Effectiveness of our proposed models is shown by numerical experiments on some benchmark data sets.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings
Pages153-161
Number of pages9
DOIs
Publication statusPublished - 2010 Nov 8
Event11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010 - Paisley, United Kingdom
Duration: 2010 Sep 12010 Sep 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6283 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010
Country/TerritoryUnited Kingdom
CityPaisley
Period10/9/110/9/3

Keywords

  • Bregman divergence
  • Copula
  • Generalized independence
  • Naive Bayes
  • Nonloglinear marginal model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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