Feature redundancy term variation for mutual information-based feature selection

Feature selection plays a critical role in many applications that are relevant to machine learning, image processing and gene expression analysis. Traditional feature selection methods intend to maximize feature dependency while minimizing feature redundancy. In previous information-theoretical-based feature selection methods, feature redundancy term is measured by the mutual information between a candidate feature and each already-selected feature or the interaction information among a candidate feature, each already-selected feature and the class. However, the larger values of the traditional feature redundancy term do not indicate the worse a candidate feature because a candidate feature can obtain large redundant information, meanwhile offering large new classification information. To address this issue, we design a new feature redundancy term that considers the relevancy between a candidate feature and the class given each already-selected feature, and a novel feature selection method named min-redundancy and max-dependency (MRMD) is proposed. To verify the effectiveness of our method, MRMD is compared to eight competitive methods on an artificial example and fifteen real-world data sets respectively. The experimental results show that our method achieves the best classification performance with respect to multiple evaluation criteria.