Multi-Label Feature Selection Based on Min-Relevance Label

Abstract

Multi-label feature selection has been widely adopted to address multi-label data with highdimension features. It is critical to calculate label correlations for multi-label feature selection. Existing methods adopt different schemes to calculate label correlations, which obtain different importance of labels. However, there exist two issues regarding these schemes calculating label importance: first, previous schemes cannot predict the whole labels well because they only focus on the most important labels; second, most of important labels have similar classification information corresponding to redundant features. To this end, we use the mutual information metric to obtain different cores regarding label set rather than calculating the importance of labels. Afterwards, we capture features with respect to each core label, finally, obtaining an optimal feature subset. To verify the effectiveness, our method is compared to state-of-the-art multi-label methods on 16 real-world data sets with several evaluating metrics. The results of experiment proves that the proposed method has achieves the best classification performance among all multi-label feature selection methods.