Supervised feature selection via multiobjective programming and its application in the medical field
Optimization Eruditorum, Volume 3, Issue 3, December 2026, Pages 153–171
Pham Thi Khanh
Faculty of Mathematics and Informatics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi, Vietnam
Pham Thi Hoai
Faculty of Mathematics and Informatics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi, Vietnam
Abstract
In this study, we model the supervised feature selection problem using a novel approach: convex bi-objective optimization. Traditional methods have addressed this problem by maximizing relevance to class labels and minimizing redundancy among features. Recently, Wang et al. [30] formulated this problem as a single-objective convex optimization, yielding only a unique solution. Unlike that, we approach this problem by preserving the natural multi-objective essence of the supervised feature selection problem, enabling a broader exploration of the objective space and providing a set of optimal solutions rather than a single result. To solve the obtained model, we utilize two state-of-the-art strategies: an exact method and a heuristic method. Additionally, we have enhanced the exact method using a scaling technique, which accelerates processing speed and expands the Pareto front. In parallel, the heuristic method ensures that the Pareto solutions achieve extensive coverage and distribution. The effectiveness of our proposed method is confirmed through standard medical datasets, demonstrating superiority over existing techniques. Notably, in the context of skin cancer screening, the method optimized the feature set to less than half of its original size, thereby significantly enhancing classification accuracy for the task.
Cite this Article as
Pham Thi Khanh and Pham Thi Hoai, Supervised feature selection via multiobjective programming and its application in the medical field, Optimization Eruditorum, 3(3), 153–171, 2026