Effective methods for improving naive Bayes text classifiers

Sang Bum Kim; Hae-Chang Rim; Dong Suk Yook; Heui Seok Lim

doi:10.1007/3-540-45683-x_45

Effective methods for improving naive Bayes text classifiers

Sang Bum Kim, Hae-Chang Rim, Dong Suk Yook, Heui Seok Lim

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

65 Citations (Scopus)

Abstract

Though naive Bayes text classifiers are widely used because of its simplicity, the techniques for improving performances of these classifiers have been rarely studied. In this paper, we propose and evaluate some general and effective techniques for improving performance of the naive Bayes text classifier. We suggest document model based parameter estimation and document length normalization to alleviate the problems in the traditional multinomial approach for text classification. In addition, Mutual-Information-weighted naive Bayes text classifier is proposed to increase the effect of highly informative words. Our techniques are evaluated on the Reuters21578 and 20 Newsgroups collections, and significant improvements are obtained over the existing multinomial naive Bayes approach.

Original language	English
Title of host publication	PRICAI 2002
Subtitle of host publication	Trends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Editors	Mitsuru Ishizuka, Abdul Sattar
Publisher	Springer Verlag
Pages	414-423
Number of pages	10
ISBN (Print)	3540440380, 9783540440383
DOIs	https://doi.org/10.1007/3-540-45683-x_45
Publication status	Published - 2002
Event	7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002 - Tokyo, Japan Duration: 2002 Aug 18 → 2002 Aug 22

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2417
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002
Country/Territory	Japan
City	Tokyo
Period	02/8/18 → 02/8/22

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45683-x_45

Cite this

Kim, S. B., Rim, H-C., Yook, D. S., & Lim, H. S. (2002). Effective methods for improving naive Bayes text classifiers. In M. Ishizuka, & A. Sattar (Eds.), PRICAI 2002: Trends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 414-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2417). Springer Verlag. https://doi.org/10.1007/3-540-45683-x_45

Effective methods for improving naive Bayes text classifiers. / Kim, Sang Bum; Rim, Hae-Chang; Yook, Dong Suk et al.
PRICAI 2002: Trends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings. ed. / Mitsuru Ishizuka; Abdul Sattar. Springer Verlag, 2002. p. 414-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2417).

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

Kim, SB, Rim, H-C, Yook, DS & Lim, HS 2002, Effective methods for improving naive Bayes text classifiers. in M Ishizuka & A Sattar (eds), PRICAI 2002: Trends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2417, Springer Verlag, pp. 414-423, 7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002, Tokyo, Japan, 02/8/18. https://doi.org/10.1007/3-540-45683-x_45

Kim SB, Rim H-C, Yook DS , Lim HS. Effective methods for improving naive Bayes text classifiers. In Ishizuka M, Sattar A, editors, PRICAI 2002: Trends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2002. p. 414-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45683-x_45

Kim, Sang Bum ; Rim, Hae-Chang ; Yook, Dong Suk et al. / Effective methods for improving naive Bayes text classifiers. PRICAI 2002: Trends in Artificial Intelligence - 7th Pacific Rim International Conference on Artificial Intelligence, Proceedings. editor / Mitsuru Ishizuka ; Abdul Sattar. Springer Verlag, 2002. pp. 414-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Effective methods for improving naive Bayes text classifiers

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