Linear Cost-sensitive Max-margin Embedded Feature Selection for SVM

作者：

Highlights：

• An efficient embedded support vector machine feature selection model.

• Cost-sensitive selection of features considering feature relevance and redundancy.

• Feature relevance is influenced by the cost sensitivity of support vector machines.

摘要

•An efficient embedded support vector machine feature selection model.•Cost-sensitive selection of features considering feature relevance and redundancy.•Feature relevance is influenced by the cost sensitivity of support vector machines.

论文关键词：Classification,Cost-sensitive learning,Feature selection,Mathematical programming,Support vector machines

论文评审过程：Received 7 June 2021, Revised 7 November 2021, Accepted 14 February 2022, Available online 23 February 2022, Version of Record 9 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116683