Label distribution feature selection for multi-label classification with rough set

Abstract Multi-label learning deals with cases where every instance corresponds to multiple labels. The objective is to learn mapping from an instance to a relevant label set. Existing multi-label learning approaches assume that the significance for all related labels is same for every instance. Several problems of label ambiguity can be dealt with using multi-label learning, but some practical applications with significance among related labels for every instance cannot be effectively processed. To achieve superior results by conducting different significance of labels, label distribution learning is used for such applications. First, the probability model and rough set are embedded in the labeling significance, thus more supervised information can be obtained from original multi-label data. Subsequently, to resolve the feature selection problem of label distribution data, according to the feature dependency and the rough set, a novel feature selection algorithm for multi-label classification is designed. Finally, to verify the effectiveness of the proposed algorithms, an extensive experiment is conducted on 15 real-world multiple label data sets. The performance of the proposed algorithm through the multi-label classifier is compared with seven state-of-the-art approaches, thereby indicating the applicability and effectiveness of label distribution feature selection.