Distinguishing two types of labels for multi-label feature selection
作者：
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.
摘要
•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.
论文关键词：Pattern recognition,Multi-label classification,Multi-label feature selection,Information theory,Label redundancy
论文评审过程：Received 24 October 2018, Revised 24 April 2019, Accepted 4 June 2019, Available online 4 June 2019, Version of Record 7 June 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.06.004
