@inproceedings{db4ec0c062f44e7585c3f7ecbbcab839,
title = "Effective methods for improving naive Bayes text classifiers",
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.",
author = "Kim, {Sang Bum} and Hae-Chang Rim and Yook, {Dong Suk} and Lim, {Heui Seok}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002 ; Conference date: 18-08-2002 Through 22-08-2002",
year = "2002",
doi = "10.1007/3-540-45683-x_45",
language = "English",
isbn = "3540440380",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "414--423",
editor = "Mitsuru Ishizuka and Abdul Sattar",
booktitle = "PRICAI 2002",
}