A novel multi-label feature selection method with association rules and rough set

In multi-label learning, each instance is associated with a set of labels. To improve the accuracy and efficiency of multi-label learning tasks, label correlations have been explored. However, existing conventional algorithms obtain label correlation directly from the raw label space, ignoring the significance of the label to the instance. In this study, a novel feature selection method was proposed to select a more relevant and compact feature subset by considering the label distribution and inter-label correlations. First, the concept of label distribution was defined to reflect the significance of each label. Second, a new algorithm for mining association rules was designed to obtain the correlation between labels by improving the existing association rules algorithm. Thus, a new information system was designed by combining the label distribution and correlation between labels. Subsequently, a novel feature selection algorithm was designed in this information system which can effectively remove irrelevant and redundant features in the feature space. Finally, the experimental results demonstrated that the proposed algorithm effectively improves the classification performance and perform better than some state-of-the-art methods.