A Master‐Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification
Enock Momanyi, Davies Segera · BioMed Research International · 2021
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).