HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search
This addresses the challenge of capturing subtle feature relationships in domains like medical classification and drug prediction, though it is incremental as it builds on existing algorithms.
The paper tackles the problem of feature selection in high-dimensional datasets by introducing the HeFS framework, which refines existing feature subsets to identify complementary features, achieving superior performance over state-of-the-art methods on 18 benchmark datasets.
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/.