Granular multi-label feature selection based on mutual information

作者：

Highlights：

• We granulate the label space into information granules to exploit label dependency.

• We present a multi-label maximal correlation minimal redundancy criterion.

• The proposed method can select compact and specific feature subsets.

• The proposed method can significantly improve the algorithm performance.

摘要

•We granulate the label space into information granules to exploit label dependency.•We present a multi-label maximal correlation minimal redundancy criterion.•The proposed method can select compact and specific feature subsets.•The proposed method can significantly improve the algorithm performance.

论文关键词：Granular computing,Feature selection,Multi-label learning,Mutual information

论文评审过程：Received 31 May 2016, Revised 17 February 2017, Accepted 18 February 2017, Available online 27 February 2017, Version of Record 6 March 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.025