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

Enock Momanyi,Davies Segera

doi:10.1155/2021/5556941

Enock Momanyi, Davies Segera

Open Access

https://doi.org/10.1155/2021/5556941

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Journal: BioMed research international	Publication Date: Oct 12, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: University of Nairobi

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).

Highlights

A number of datasets especially of biomedical nature are high dimensional
This paper focuses on the wrapper method for feature selection
In an effort to improve the exploration ability of GWO to escape the local optimum, this paper proposes a master-slave binary grey wolf optimizer (MSBGWO) algorithm

Summary

Introduction

A number of datasets especially of biomedical nature are high dimensional. This means that they have a high number of features per sample. The Powell local optimization method is introduced to the GWO for clustering analysis and compared to some evolutionary algorithms; the results were better on the benchmark functions and datasets considered [25]. Despite these improvements, no method has been able to exhaustively find the optimal solution when it comes to feature selection. In an effort to improve the exploration ability of GWO to escape the local optimum, this paper proposes a master-slave binary grey wolf optimizer (MSBGWO) algorithm This proposed methodology alters the position of the wolves during exploration and exploitation and ensures diversification of the solutions to be considered.

Grey Wolf Optimizer

Experimental Design

Experimental Results and Discussion

Conclusion