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

TL;DR

HeFS is a novel feature selection framework that enhances existing methods by systematically identifying complementary features using Pareto-optimized genetic search, leading to improved classification performance on high-dimensional datasets.

Contribution

This paper introduces HeFS, a helper-enhanced feature selection method that refines feature subsets through Pareto-optimized genetic search, addressing limitations of heuristic approaches.

Findings

HeFS outperforms state-of-the-art methods on 18 benchmark datasets.

HeFS effectively identifies overlooked informative features.

HeFS improves classification accuracy in complex domains like cancer and drug toxicity.

Abstract

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. The code and datasets are available at https://healthinformaticslab.org/supp/.