The rapid advancement of technology and the exponential increase in data volumes have emphasized the critical significance of effective feature selection (FS) methods in data mining. FS aims to eliminate redundant and irrelevant features from datasets while maintaining or improving the accuracy of classification models. All past studies on the FS problem have tried to improve parameters such as classification accuracy and the size of chosen feature subsets. Despite their success, there is still opportunity to improve these parameters further by selecting newer and more efficient algorithms. In this context, this paper proposes an innovative approach to FS utilizing the Binary Human Mental Search (BHMS), a novel algorithm that has not previously been used in FS. The method introduced in this paper adapts the HMS algorithm into a binary framework specifically tailored to identify optimal feature subsets for classification tasks. The K-Nearest Neighbors (KNN) classifier is also employed as the classification technique in data mining. The experimental evaluations are conducted on standard benchmark datasets from the UCI Dataset Collection. The performance of the proposed BHMS method is compared against nine advanced metaheuristic methods. Results indicate that the BHMS variant achieves competitive performance, demonstrating its effectiveness in feature selection for classification tasks. Parameters such as the average classification accuracy, the average size of the chosen feature subsets, the average fitness, and the value of the Wilcoxon test has improved significantly compared to comparative algorithms. In summary, this paper emphasizes the critical necessity for effective feature selection methods in the age of big data and technological progress. The proposed BHMS algorithm presents a promising approach to identifying optimal feature subsets, contributing to improved classification accuracy in data mining applications.
Farsani et al. (Fri,) studied this question.