Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection

作者：

Highlights：

• HPSO-SSM is proposed based on original particle swarm optimization.

• A new wrapper-based feature selection approach based on HPSO-SSM is proposed.

• The logistic map sequence is used to enhance the diversity in the search process.

• An innovative position update model is presented to improve the position quality.

• Our method outperforms seventeen extremely competitive methods in terms of accuracy.

摘要

•HPSO-SSM is proposed based on original particle swarm optimization.•A new wrapper-based feature selection approach based on HPSO-SSM is proposed.•The logistic map sequence is used to enhance the diversity in the search process.•An innovative position update model is presented to improve the position quality.•Our method outperforms seventeen extremely competitive methods in terms of accuracy.

论文关键词：Particle swarm optimization,Feature selection,Classification,Optimization

论文评审过程：Received 22 June 2018, Revised 21 March 2019, Accepted 21 March 2019, Available online 23 March 2019, Version of Record 30 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.039