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

• URL: http://arxiv.org/abs/2510.18575v1
• Date: Tue, 21 Oct 2025 12:30:22 GMT
• Title: HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search
• Authors: Yusi Fan, Tian Wang, Zhiying Yan, Chang Liu, Qiong Zhou, Qi Lu, Zhehao Guo, Ziqi Deng, Wenyu Zhu, Ruochi Zhang, Fengfeng Zhou,
• Abstract summary: We introduce the HeFS (Helper-Enhanced Feature Selection) framework to refine feature subsets produced by existing algorithms.<n>HeFS systematically searches the residual feature space to identify a Helper Set - features that complement the original subset and improve classification performance.<n> Experiments on 18 benchmark datasets demonstrate that HeFS consistently identifies yet informative features and achieves superior performance over state-of-the-art methods.
• Score: 10.751560953850925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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/.

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