A Euclidean Distance-Based Novel Algorithm for Binary Feature Selection

Feature selection, a critical technique for identifying optimal feature subsets, enhances model accuracy while reducing feature dimensionality. Although the recently proposed metaheuristic Horned Lizard Optimization Algorithm (HLOA) exhibits robust stochastic search capabilities for complex optimization problems, it is inherently incompatible with discrete binary tasks such as feature selection. While sigmoid functions conventionally bridge this gap, this paper introduces an innovative Euclidean-distance-based binarization mechanism and its enhanced variant to adapt HLOA's superior search performance to feature selection. Experimental validation across 20 UCI (University of California, Irvine) benchmark datasets demonstrates the efficacy of the proposed methods. Notably, on high-dimensional datasets (dimensionality > 1,000), our algorithms achieve significant reductions in feature size while consistently improving predictive accuracy.