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

Enock Momanyi¹, Davies Segera¹

¹Department of Electrical and Information Engineering, University of Nairobi, Nairobi 30197, Kenya.

Biomed Research International

|October 22, 2021

Summary

A novel master-slave binary grey wolf optimizer (MSBGWO) enhances feature selection in biomedical datasets. This improved algorithm offers superior classification accuracy and efficiency compared to existing methods.

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Area of Science:

Computational Intelligence
Bioinformatics
Machine Learning

Background:

Feature selection is crucial for improving classification performance in high-dimensional biomedical datasets.
Existing metaheuristic algorithms face challenges in effectively exploring search spaces for optimal feature subsets.

Purpose of the Study:

To introduce a new metaheuristic algorithm, the master-slave binary grey wolf optimizer (MSBGWO).
To evaluate the efficacy of MSBGWO for feature selection in high-dimensional biomedical data.

Main Methods:

Implementation of a master-slave learning scheme within the binary grey wolf optimizer (GWO).
Testing MSBGWO on five high-dimensional biomedical datasets.
Comparative analysis against Binary Grey Wolf Optimizer version 2 (BGWO2), Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), Differential Evolution (DE), and Sine-Cosine Algorithm (SCA).

Main Results:

MSBGWO demonstrated superior performance across key metrics: classification accuracy, precision, recall, and F-measure.
The algorithm achieved a more effective reduction in the number of selected features.
MSBGWO outperformed all compared algorithms in feature selection tasks.

Conclusions:

The proposed MSBGWO algorithm significantly enhances the exploration capability of the GWO.
MSBGWO is a highly effective tool for feature selection in complex biomedical datasets.
This advancement offers improved diagnostic and analytical capabilities in bioinformatics.