A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

作者：

Highlights：

• Anew Grey Wolf Optimizer algorithm with a Two-phase Mutation (TMGWO) is proposed.

• Wrapper-based feature selection techniques are proposed using TMGWO algorithm.

• The proposed algorithm is benchmarked on 35 standard UCI datasets.

• TMGWO algorithm is compared with recent state-of-the-art algorithms.

• A superior performance of the proposed algorithm is proved in the experiments.

摘要

•Anew Grey Wolf Optimizer algorithm with a Two-phase Mutation (TMGWO) is proposed.•Wrapper-based feature selection techniques are proposed using TMGWO algorithm.•The proposed algorithm is benchmarked on 35 standard UCI datasets.•TMGWO algorithm is compared with recent state-of-the-art algorithms.•A superior performance of the proposed algorithm is proved in the experiments.

论文关键词：Feature selection,Grey wolf optimization algorithm,Wrapper method,Classifier, accuracy,Cross-validation,Mutation

论文评审过程：Received 6 April 2019, Revised 17 July 2019, Accepted 18 July 2019, Available online 27 July 2019, Version of Record 31 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112824