An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets

Abstract

Feature selection (FS) constitutes a crucial endeavor in classification procedures, aiming to identify the minimal subset of features that maximizes classification accuracy. In the realm of combinatorial NP-hard challenges, FS assumes significance, prompting the utilization of robust metaheuristics as efficient wrapper-based FS strategies. However, the application of these wrapper metaheuristics to high-dimensional datasets, characterized by an abundance of features and a scarcity of samples, often results in a decline in effectiveness and escalated computational costs. Addressing these limitations, this study introduces the Adaptive Hybrid-Mutated Differential Evolution (A-HMDE) method, targeting the inherent drawbacks of the Differential Evolution (DE) algorithm. The A-HMDE incorporates four distinct strategies. Firstly, it integrates the mechanics of the Spider Wasp Optimization (SWO) algorithm into DE’s mutation strategies, yielding enhanced performance marked by high accuracy and swift convergence towards global optima. Secondly, adaptive mechanisms are applied to key DE parameters, amplifying the efficiency of the search process. Thirdly, an adaptive mutation operator ensures a harmonious balance between global exploration and local exploitation during optimization. Lastly, the concept of Enhanced Solution Quality (ESQ), rooted in the RUN algorithm, guides DE to elude local optima, thus heightening the accuracy of obtained solutions. The efficiency of the A-HMDE approach is assessed by employing it as a FS method across diverse datasets encompassing the UCI repository, microarray data, and skin disease image dataset. The experimental results accentuate the method’s remarkable ability to overcome challenges related to local minima and hasten the convergence process. A comprehensive comparison against contemporary cutting-edge algorithms highlights the considerable advancements achieved by the proposed method, with recorded accuracy ranging from 0.88 to 1.00.