A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification. (12th October 2021)

• Record Type: Journal Article
• Authors: Momanyi, Enock; Segera, Davies
• Other Names: Tang Chang Academic Editor.
• Is Part Of: BioMed research international. Volume 2021(2021)
• Publication Date: 2021-10-12
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2021/5556941
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and sine-cosine algorithm (SCA).