An Idea of setting weighting functions for feature selection

In this paper, we propose a novel feature selection method, which improves effectively traditional mutual information based feature selection. The method takes as the first step traditional mutual information based feature selection. Then the method multiplies each feature by a weighting coefficient that is directly related to the mutual information value between the feature and class labels. Finally the multiplication results of the features with large mutual values are used as final features for classification. The result of nearest neighbor (NN) classification on spam emails filter and prediction of molecular bioactivity shows that the proposed method is able to improve the performance of NN classification. In additional, using fewer features NN classification is capable of achieving the same accuracy as NN classification using all of original features.