Feature selection with kernelized multi-class support vector machine

作者：

Highlights：

• We propose a new nonlinear feature selection method based on kernelized multiclass support vector machine (COMSVM).

• The recursive feature elimination algorithm is improved by adding the batch elimination and the rescreening process.

• The fast recursive feature elimination algorithm is implemented with the proposed COMSVM.

• The experiment results demonstrate the efficiency of our new method.

摘要

•We propose a new nonlinear feature selection method based on kernelized multiclass support vector machine (COMSVM).•The recursive feature elimination algorithm is improved by adding the batch elimination and the rescreening process.•The fast recursive feature elimination algorithm is implemented with the proposed COMSVM.•The experiment results demonstrate the efficiency of our new method.

论文关键词：Feature selection,Multi-class support vector machine,Kernel machine,Recursive feature elimination

论文评审过程：Received 26 May 2020, Revised 16 March 2021, Accepted 5 April 2021, Available online 20 April 2021, Version of Record 7 May 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.107988