Improved mutual information method for text feature selection

Reducing the dimensions of high-dimensional feature set is one of the difficulties of text categorization. Feature selection has been effectively applied in text classification, because of its low complexity of computing. Research works show that mutual information is a good feature selection method but doesn´t consider the term frequency in each category of the corpus and the connections between terms. To remedying the defects of traditional mutual information method, this article improved measure of mutual information by introducing the feature frequency in class and the dispersion of feature in class, and built a experimental platform by constructing a Chinese text classification system, and did a multi-set of experiments base on this system. The results show that the new feature selection approach has a more excellent effect in text categorization.