A novel self-learning feature selection approach based on feature attributions

作者：

Highlights：

• A unified local search framework is proposed for feature selection wrappers.

• Neighborhoods of the local search effect the exploration and exploitation.

• Feature attributions can improve the efficiency to search for optimal subsets.

摘要

•A unified local search framework is proposed for feature selection wrappers.•Neighborhoods of the local search effect the exploration and exploitation.•Feature attributions can improve the efficiency to search for optimal subsets.

论文关键词：Feature selection,Local search,Feature attribution,Self-learning

论文评审过程：Received 7 October 2020, Revised 21 April 2021, Accepted 13 May 2021, Available online 7 June 2021, Version of Record 11 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115219