A Variance-mean Based Feature Selection in Text Classification

Feature selection is an important process to choose a subset of features relevant to a particular application in text classification. Based on the mutual information method, we designed variance-mean based feature selection (VM). After computing and ranking the variance of class discrimination value vector for each word, we can choose the most distinguishable features. This method has advantages in the case of choosing smaller number of features, especially for classes with small number of training documents. It keeps the best features, and thus improves the final performance of the classification system. The experiment results indicate the effectiveness of the proposed feature selection method in a text classification.