Discrimination-based feature selection for multinomial naive Bayes text classification

In this paper we focus on the problem of class discrimination issues to improve performance of text classification, and study a discrimination-based feature selection technique in which the features are selected based on the criterion of enlarging separation among competing classes, referred to as discrimination capability. The proposed approach discards features with small discrimination capability measured by Gaussian divergence, so as to enhance the robustness and the discrimination power of the text classification system. To evaluation its performance, some comparison experiments of multinomial naive Bayes classifier model are constructed on Newsgroup and Ruters21578 data collection. Experimental results show that on Newsgroup data set divergence measure outperforms MI measure, and has slight better performance than DF measure, and outperforms both measures on Ruters21578 data set. It shows that discrimination-based feature selection method has good contributions to enhance discrimination power of text classification model.