Synchronized feature selection for Support Vector Machines with twin hyperplanes

作者：

Highlights：

• Novel feature selection approach for SVM classification.

• The Nonparallel Hyperplane SVM formulation is used to solve the twin subproblems.

• L-infinity norm used as regularized for coordinated variable elimination.

• Superior performance is achieved in experiments on microarray datasets.

摘要

•Novel feature selection approach for SVM classification.•The Nonparallel Hyperplane SVM formulation is used to solve the twin subproblems.•L-infinity norm used as regularized for coordinated variable elimination.•Superior performance is achieved in experiments on microarray datasets.

论文关键词：Support vector machine,Embedded methods,Feature selection,L-infinity norm

论文评审过程：Received 20 January 2017, Revised 11 May 2017, Accepted 16 June 2017, Available online 17 June 2017, Version of Record 24 July 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.06.025