Novel binary walrus optimization algorithms BWaOA and BWaOA-C with crossover operator for feature selection in high-dimensional data

Abstract

Redundant and irrelevant features in high-dimensional datasets hinder the development of efficient machine learning models. Most existing Feature Selection (FS) algorithms are developed based on either embedded or filter techniques, which makes it challenging to identify the highly discriminant features due to limited search capability and high computational cost. To overcome these challenges, we propose a novel wrapper-based FS framework built on the Walrus Optimization Algorithm (WaOA) to balance accuracy and efficiency. The key novelties of our framework include two advanced binarization strategies: Binary WaOA (BWaOA), which uses S- and V-shaped transfer functions for effective search space discretization, and Binary WaOA-Crossover (BWaOA-C), which incorporates crossover operators to improve exploration, diversity, and refinement. Unlike conventional approaches, our methods systematically combine adaptive transfer functions and dynamic thresholding to select compact yet highly discriminative feature subsets, evaluated using a K-Nearest Neighbors (KNN) classifier. Extensive experiments on 30 benchmark datasets demonstrate the superiority of the proposed framework against 12 state-of-the-art FS algorithms, including GA, PSO, HHO, GWO, ChOA, BDE, WOA, AMGWO, BTLBO-KNN, HLBDA, BABC, and RGA-T. BWaOA achieves 86.33% feature reduction and 92.56% classification accuracy, while BWaOA-C further improves accuracy by up to 7%. These findings demonstrate the robustness and practical effectiveness of the proposed framework for high-dimensional data analysis.