Distinguishing two types of labels for multi-label feature selection

Abstract

Abstract Multi-label feature selection plays an important role in pattern recognition, which can improve multi-label classification performance. In traditional multi-label feature selection methods based on information theory, feature relevance is evaluated by the accumulated mutual information between a candidate feature and each label. However, to the best of our knowledge, traditional methods ignore the effect of label redundancy on the evaluation of feature relevance. To address this issue, we propose a new multi-label feature selection method named multi-label Feature Selection based on Label Redundancy (LRFS). First, we categorize labels into two groups: independent labels and dependent labels. Second, by analyzing the differences between independent labels and dependent labels, we propose a new feature relevance term, that is, the conditional mutual information between candidate features and each label given other labels. Finally, we combine the new feature relevance term with the feature redundancy term to design our feature selection method. To evaluate the classification performance of our method, LRFS is compared to three information-theoretical-based multi-label feature selection methods on an artificial data set. Furthermore, LRFS is compared to five algorithm adaption feature selection methods and two problem transformation feature selection methods on 12 real-world multi-label data sets. The experimental results demonstrate that LRFS outperforms the other compared methods in terms of four evaluation metrics.