Effective methods for improving naive Bayes text classifiers

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45 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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages414-423
Number of pages10
Volume2417
ISBN (Print)3540440380, 9783540440383
Publication statusPublished - 2002
Event7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002 - Tokyo, Japan
Duration: 2002 Aug 182002 Aug 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2417
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002
CountryJapan
CityTokyo
Period02/8/1802/8/22

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Kim, S. B., Rim, H-C., Yook, D., & Lim, H. S. (2002). Effective methods for improving naive Bayes text classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2417, pp. 414-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2417). Springer Verlag.