A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification

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...

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Bibliographic Details
Published in:BioMed research international 2021, Vol.2021 (1), p.5556941-5556941
Main Authors: Momanyi, Enock, Segera, Davies
Format: Article
Language:English
Subjects:
Algorithms
Biomedical data
Classification
Computer Simulation
Databases, Factual
Datasets
Electronic data processing
Evolutionary algorithms
Evolutionary computation
Feature selection
Female
Genetic algorithms
Humans
Information management
Machine Learning
Male
Mathematical optimization
Medical diagnosis
Medical Informatics - methods
Medical research
Medicine, Experimental
Methods
Neoplasms - classification
Neoplasms - metabolism
Neoplasms - pathology
Optimization algorithms
Particle swarm optimization
Pattern Recognition, Automated - methods
Population
Swarm intelligence
Trigonometric functions
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Summary: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).
ISSN:2314-6133
2314-6141
DOI:10.1155/2021/5556941