A feature selection method via relevant-redundant weight

作者：

Highlights：

• Design a feature relevant-redundant weight and define feature relevance.

• Construct a novel feature evaluation criterion named FSRRW.

• FSRRW is evaluated on 20 benchmark datasets under three classifiers.

• The proposed method outperforms seven popular feature selection methods.

摘要

•Design a feature relevant-redundant weight and define feature relevance.•Construct a novel feature evaluation criterion named FSRRW.•FSRRW is evaluated on 20 benchmark datasets under three classifiers.•The proposed method outperforms seven popular feature selection methods.

论文关键词：Information theory,Feature selection,Feature relevant-redundant weight,Feature relevance

论文评审过程：Received 31 January 2022, Revised 17 May 2022, Accepted 17 June 2022, Available online 23 June 2022, Version of Record 1 July 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117923