Abstract: Multi-label data widely exists in the real world, and multi-label feature selection is an important preliminary step in multi-label learning. Based on the fuzzy rough set model, researchers have proposed multi-label feature selection algorithms, but most of these algorithms do not pay attention to the co-occurrence characteristics between labels. In order to solve this problem, the similar relationship between the samples under the label set is evaluated based on the co-occurrence relationship between the sample labels. This relationship is used to define the fuzzy mutual information between the feature and the label. Combining the principle of maximum correlation and minimum redundancy, a multi-label feature selection algorithm is designed. Experiments on 5 public data sets show the effectiveness of the proposed algorithm.

Key words: multi-label, feature selection, fuzzy rough set, fuzzy mutual information