Binary chemical reaction optimization based feature selection techniques for machine learning classification problems

作者：

Highlights：

• A chemical reaction optimization (CRO) based feature selection (FS) technique is proposed.

• The proposed CRO based FS technique is improvised using particle swarm optimization.

• Performance evaluation of proposed techniques on benchmark datasets gives promising results.

摘要

•A chemical reaction optimization (CRO) based feature selection (FS) technique is proposed.•The proposed CRO based FS technique is improvised using particle swarm optimization.•Performance evaluation of proposed techniques on benchmark datasets gives promising results.

论文关键词：Machine learning (ML),Feature selection (FS),Chemical reaction optimization (CRO),Particle swarm optimization (PSO),Genetic algorithm (GA),Ant colony optimization (ACO)

论文评审过程：Received 29 July 2020, Revised 15 September 2020, Accepted 24 October 2020, Available online 8 November 2020, Version of Record 10 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114169