An improved group teaching optimization algorithm based on local search and chaotic map for feature selection in high-dimensional data
作者：
Highlights：
• Feature selection method based on Group Teaching Optimization Algorithm is proposed.
• Two novel operators are developed to increase the exploration and exploitation.
• The student phase is enhanced to increase the population diversity and exploitation.
• The teacher allocation phase is enhanced to increase the convergence rate.
• The superiority of the proposed algorithm is indicated over 30 benchmark datasets.
摘要
•Feature selection method based on Group Teaching Optimization Algorithm is proposed.•Two novel operators are developed to increase the exploration and exploitation.•The student phase is enhanced to increase the population diversity and exploitation.•The teacher allocation phase is enhanced to increase the convergence rate.•The superiority of the proposed algorithm is indicated over 30 benchmark datasets.
论文关键词：Feature selection,Binary group teaching optimization algorithm,Local search,Chaos mapping,S-shaped and V-shaped transfer functions
论文评审过程：Received 27 December 2021, Revised 18 April 2022, Accepted 30 April 2022, Available online 10 May 2022, Version of Record 25 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117493
