A generalization of independence in naive bayes model

Yu Fujimoto; Noboru Murata

doi:10.1007/978-3-642-15381-5_19

A generalization of independence in naive bayes model

Yu Fujimoto^*, Noboru Murata

^*Corresponding author for this work

School of Advanced Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Intelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings
Pages	153-161
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-15381-5_19
Publication status	Published - 2010 Nov 8
Event	11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010 - Paisley, United Kingdom Duration: 2010 Sept 1 → 2010 Sept 3

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6283 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010
Country/Territory	United Kingdom
City	Paisley
Period	10/9/1 → 10/9/3

Keywords

Bregman divergence
Copula
Generalized independence
Naive Bayes
Nonloglinear marginal model

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-15381-5_19

Cite this

Fujimoto, Y., & Murata, N. (2010). A generalization of independence in naive bayes model. In Intelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings (pp. 153-161). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6283 LNCS). https://doi.org/10.1007/978-3-642-15381-5_19

A generalization of independence in naive bayes model. / Fujimoto, Yu; Murata, Noboru.
Intelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings. 2010. p. 153-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6283 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fujimoto, Y & Murata, N 2010, A generalization of independence in naive bayes model. in Intelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6283 LNCS, pp. 153-161, 11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, Paisley, United Kingdom, 10/9/1. https://doi.org/10.1007/978-3-642-15381-5_19

Fujimoto Y, Murata N. A generalization of independence in naive bayes model. In Intelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings. 2010. p. 153-161. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15381-5_19

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A generalization of independence in naive bayes model

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