关键词: 文本分类, 信息增益, 特征选择, 类内分散度, 类间集中度, 比例因子

Abstract: The feature selection method of traditional Information Gain（IG） ignoring the shortcoming of distributing information inside class and between classes is analysed. Distribution information inside class and concentration information between classes are introduced, which is  used to distinguish characteristics of strong correlation with class. Considering the problem of the feature selection method of traditional Information Gain（IG） not well combining positive feature and negative feature, the ratio of positive feature and negative feature is introduced with proportional factor to balance the effect of feature appear and disappear, which decreases the effect of negative feature on the corpus of category uneven distribution and increases classification effect. The experimental results verify the efficiency and probability of the improved IG approach.

Key words: text categorization, information gain, feature selection, distribution information inside class, Concentration information between classes, proportional factor