Tri-staged feature selection in multi-class heterogeneous datasets using memetic algorithm and cuckoo search optimization
作者：
Highlights：
• Proposes Tri-Staged Feature Selection (TFS) for multi-class heterogeneous datasets.
• Initial features are selected using Kruskal Wallis Test.
• Refinement of obtained features using Memetic Algorithm with local beam search.
• Final feature set refinement using Cuckoo search algorithm for better classification.
• Experiments conducted on 12 real datasets for validation of proposed method.
摘要
•Proposes Tri-Staged Feature Selection (TFS) for multi-class heterogeneous datasets.•Initial features are selected using Kruskal Wallis Test.•Refinement of obtained features using Memetic Algorithm with local beam search.•Final feature set refinement using Cuckoo search algorithm for better classification.•Experiments conducted on 12 real datasets for validation of proposed method.
论文关键词：Cuckoo search,Memetic algorithm,Kruskal Wallis test,Local beam search,Heterogeneous datasets
论文评审过程：Received 1 July 2020, Revised 6 July 2022, Accepted 24 July 2022, Available online 29 July 2022, Version of Record 9 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118286
