{"ID":2852241,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18575","arxiv_id":"2510.18575","title":"HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search","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/.","short_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, wher...","url_abs":"https://arxiv.org/abs/2510.18575","url_pdf":"https://arxiv.org/pdf/2510.18575v1","authors":"[\"Yusi Fan\",\"Tian Wang\",\"Zhiying Yan\",\"Chang Liu\",\"Qiong Zhou\",\"Qi Lu\",\"Zhehao Guo\",\"Ziqi Deng\",\"Wenyu Zhu\",\"Ruochi Zhang\",\"Fengfeng Zhou\"]","published":"2025-10-21T12:30:22Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.QM\"]","methods":"[]","project_urls":"[\"https://healthinformaticslab.org/supp/\"]","has_code":false}