A dynamic support ratio of selected feature-based information for feature selection

Abstract

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. Existing methods concentrate on the classification information from candidate features while seldom considering the changing information supported by selected features. In this paper, we construct a dynamic support ratio (DSR), which employs the new information of selected features to support classification. DSR explicitly describes the dynamic interactions between selected features and candidate features. Simultaneously, the feature relevance and feature redundancy are treated adaptively. Thus, distinctive features can be noticed sensitively. Afterward, a novel feature selection method based on a dynamic support ratio (DSRFS) is proposed. The proposed method is established on 18 benchmark data sets with four different classifiers. Classification accuracy, standard deviation, recall and statistical validations are employed to measure the classification performance. Extensive experiments demonstrate that DSRFS not only reduces the dimension of the feature space effectively, but also obtains the best average classification accuracy.