HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909861525061632 |
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| author | Fan, Yusi Wang, Tian Yan, Zhiying Liu, Chang Zhou, Qiong Lu, Qi Guo, Zhehao Deng, Ziqi Zhu, Wenyu Zhang, Ruochi Zhou, Fengfeng |
| author_facet | Fan, Yusi Wang, Tian Yan, Zhiying Liu, Chang Zhou, Qiong Lu, Qi Guo, Zhehao Deng, Ziqi Zhu, Wenyu Zhang, Ruochi Zhou, Fengfeng |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search Fan, Yusi Wang, Tian Yan, Zhiying Liu, Chang Zhou, Qiong Lu, Qi Guo, Zhehao Deng, Ziqi Zhu, Wenyu Zhang, Ruochi Zhou, Fengfeng Machine Learning Quantitative Methods 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/. |
| title | HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2510.18575 |