Feature selection method based on category discriminability

In text classification, dimension reduction on the original features space is very necessary to improve accuracy and efficiency. Feature selection is a kind of simple and effective methods in dimension reduction. In this paper, we designed two feature selection methods based on the feature´s category discriminability (CD) and the influence of features cooccurrence to classification. The experiment show that our methods proposed is much better than traditional methods and the classification results have respectively improved by 5% and 6.9% at most.