A feature selection method via relevant-redundant weight.

Feature selection is a crucial preprocessing technique in data mining and machine learning and has attracted increasing attentions. However, the relevance of existing methods only contains limited information. To enhance the classification ability of filter and information theory-based feature selection methods and reduce the redundancy of the selected subset, a relevant-redundant weight-based feature criterion (FSRRW) is proposed. In this paper, a feature relevant-redundant weight (RRW) is constructed to extract the important relevant and redundant information. Then, a novel feature relevance is defined based on the weight, which contains more comprehensive information from the dynamically changing features. Additionally, a feature evaluation criterion is presented via maximizing the feature relevance and minimizing the feature redundancy. The proposed algorithm and seven compared methods are tested on 20 benchmark datasets. Extensive experiments demonstrate that the proposed criterion exhibits better feature screening abilities, effectively facilitates classification, and has preferable applicability and robustness. • Design a feature relevant-redundant weight and define feature relevance. • Construct a novel feature evaluation criterion named FSRRW. • FSRRW is evaluated on 20 benchmark datasets under three classifiers. • The proposed method outperforms seven popular feature selection methods. [ABSTRACT FROM AUTHOR]