A multi-label feature selection method based on an approximation of interaction information

Abstract

High-dimensional multi-label data is widespread in practical applications, which brings great challenges to the research field of pattern recognition and machine learning. Many feature selection algorithms have been proposed in recent years, among which the filtering feature selection algorithm is the most popular one because of its simplicity. Therefore, filtering feature selection has become a hot research topic, especially the multi-label feature selection algorithm based on mutual information. In the algorithm, the computation cost of high dimensional mutual information is expensive. How to approximate high order mutual information based on low order mutual information has become a major research direction. To our best knowledge, all existing feature selection algorithms that consider the label correlation will increase the computational cost greatly. Therefore, this paper proposes an approximation method of three-dimensional interaction information, which is applied to the calculation of correlation and redundancy. It can take the correlation of labels into account and don’t increase the computation cost significantly at the same time. Experiments analysis results show that the proposed method is effective.