HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search

Feature selection is a combinatorial optimization problem that is NP -hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature convergence and may fail to capture subtle yet informative features. This limitation becomes especially critical in high - dimensional datasets, where complex and interdependent feature relationships prevail. We introduce the HeFS (Helper - Enhanced Feature Selection) framework to refine feature subsets produced by existing algorithms. HeFS systematically searches the residual feature space to identify a Helper Set-- features that complement the original subset and improve classification performance. The approach employs a biased initialization scheme and a ratio-guided mutation mechanism within a genetic algorithm, coupled with Pareto - based multi - objective optimization to jointly maximize predictive accuracy and feature complementarity. Experiments on 18 benchmark datasets demonstrate that HeFS consistently identifies overlooked yet informative features and achieves superior performance over state-of-the - art methods, including in challenging domains such as gastric cancer classification, drug toxicity prediction, and computer science applications.