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

arXiv: https://arxiv.org/abs/2510.18575 · HTML: https://mlsanity.com/paper/2510.18575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 56 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Multi-Objective Genetic Algorithm for Multi-View Feature Selection](https://mlsanity.com/paper/2305.18352/) (2023)

## Abstract (arXiv)

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/.