Feature selection using dynamic weights for classification

Feature selection aims at finding a feature subset that has the most discriminative information from the original feature set. In this paper, we firstly present a new scheme for feature relevance, interdependence and redundancy analysis using information theoretic criteria. Then, a dynamic weighting-based feature selection algorithm is proposed, which not only selects the most relevant features and eliminates redundant features, but also tries to retain useful intrinsic groups of interdependent features. The primary characteristic of the method is that the feature is weighted according to its interaction with the selected features. And the weight of features will be dynamically updated after each candidate feature has been selected. To verify the effectiveness of our method, experimental comparisons on six UCI data sets and four gene microarray datasets are carried out using three typical classifiers. The results indicate that our proposed method achieves promising improvement on feature selection and classification accuracy.