Efficient high-dimension feature selection based on enhanced equilibrium optimizer

作者：

Highlights：

• Enhance EO algorithm using ReliefF algorithm and the local search strategy.

• Propose new feature selection method based on a hybridization RBEO-LS method.

• Evaluate RBEO-LS using 16 UCI datasets and 10 high dimensional biological datasets.

• Results show superiority of RBEO-LS among other state-of-the-art methods.

摘要

•Enhance EO algorithm using ReliefF algorithm and the local search strategy.•Propose new feature selection method based on a hybridization RBEO-LS method.•Evaluate RBEO-LS using 16 UCI datasets and 10 high dimensional biological datasets.•Results show superiority of RBEO-LS among other state-of-the-art methods.

论文关键词：Feature selection,Equilibrium Optimizer,High-dimension Data,ReliefF,Local search strategy

论文评审过程：Received 4 August 2020, Revised 30 June 2021, Accepted 5 September 2021, Available online 10 September 2021, Version of Record 20 September 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115882