Distinguishing two types of labels for multi-label feature selection. (November 2019)

• Record Type: Journal Article
• Authors: Zhang, Ping; Liu, Guixia; Gao, Wanfu
• Is Part Of: Pattern recognition. Volume 95(2019:Nov.)
• Page Start: 72
• Page End: 82
• Publication Date: 2019-11
• Subjects: Pattern recognition -- Multi-label classification -- Multi-label feature selection -- Information theory -- Label redundancy; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.06.004
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We categorize labels into two groups: independent labels and dependent labels. A new feature relevance term that considers label redundancy is proposed. We propose a novel multi-label feature selection method. Our method outperforms seven other methods in terms of four metrics. We implement experiments on 12 benchmark data sets. 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.