An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

作者：

Highlights：

• Every growing data volume also incorporate extraordinary dimensions.

• Hybrid sine-cosine Harris hawks optimization (SCHHO) is proposed for feature selection.

• Results of numerical problems and feature selection reveal efficacy of SCHHO.

• SCHHO outperforms other counterparts and hybrid approached from recent literature.

摘要

•Every growing data volume also incorporate extraordinary dimensions.•Hybrid sine-cosine Harris hawks optimization (SCHHO) is proposed for feature selection.•Results of numerical problems and feature selection reveal efficacy of SCHHO.•SCHHO outperforms other counterparts and hybrid approached from recent literature.

论文关键词：Feature selection,Harris hawks optimization,Sine-cosine algorithm,High-dimensional data,Optimization problems

论文评审过程：Received 3 July 2020, Revised 15 December 2020, Accepted 20 February 2021, Available online 4 March 2021, Version of Record 31 March 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114778