A Probabilistic Approach to Feature Selection for Multi-class Text Categorization

Abstract. In this paper, we propose a probabilistic approach to feature selection for multi-class text categorization. Specifically, we regard document class and occurrence of each feature as events, calculate the probability of occurrence of each feature by the theorem on the total probability and utilize the values as a ranking criterion. Experiments on Reuters-2000 collection show that the proposed method can yield better performance than information gain and χ-square, which are two wellknown feature selection methods.

• Type: Conference
• Year: 2007
• Where: ISNN
• Authors: Ke Wu, Bao-Liang Lu, Masao Uchiyama, Hitoshi Isahara
• Keywords: Feature Selection, ISNN 2007, Multi-class Text Categorization, Wellknown Feature Selection