Multi-label learning with label-specific feature reduction

作者：

Highlights：

• We propose two multi-label learning approaches with LIFT reduction.

• The idea of fuzzy rough set attribute reduction is adopted in our approaches.

• Sample selection improves the efficiency in feature dimension reduction.

摘要

•We propose two multi-label learning approaches with LIFT reduction.•The idea of fuzzy rough set attribute reduction is adopted in our approaches.•Sample selection improves the efficiency in feature dimension reduction.

论文关键词：Feature reduction,Fuzzy rough set,Label-specific feature,Multi-label learning,Sample selection

论文评审过程：Received 22 December 2015, Revised 10 March 2016, Accepted 13 April 2016, Available online 25 April 2016, Version of Record 20 May 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.04.012