Feature selection for multi-label learning using mutual information and GA

Abstract

As in the traditional single-label classification, the feature selection plays an important role in the multi-label classification. This paper presents a multi-label feature selection algorithm MLFS which consists of two steps. The first step employs the mutual information to complete the local feature selection. Based on the result of local selection, GA algorithm is adopted to select the global optimal feature subset and the correlations among the labels are considered. Compared with other multi-label feature selection algorithms, MLFS exploits the label correlation to improve the performance. The experiments on two multi-label datasets demonstrate that the proposed method has been proved to be a promising multi-label feature selection method.