Improving naive Bayes text classifier using smoothing methods

Authors:
Feng He;Xiaoqing Ding
Affiliations:
Dept. of Electronic Engineering, Tsinghua University, Beijing, China and State Key Laboratory of Intelligent Technology and Systems;Dept. of Electronic Engineering, Tsinghua University, Beijing, China and State Key Laboratory of Intelligent Technology and Systems
Venue:
ECIR'07 Proceedings of the 29th European conference on IR research
Year:
2007

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Abstract

The performance of naive Bayes text classifier is greatly influenced by parameter estimation, while the large vocabulary and scarce labeled training set bring difficulty in parameter estimation. In this paper, several smoothing methods are introduced to estimate parameters in naive Bayes text classifier. The proposed approaches can achieve better and more stable performance than Laplace smoothing