A Robust Wrapper-Based Feature Selection Technique Using Real-Valued Triangulation Topology Aggregation Optimizer

Feature selection is a critical preprocessing technique used to remove irrelevant and redundant features from datasets while maintaining or improving the accuracy of machine learning models. Recent advancements in this area have primarily focused on wrapper-based feature selection methods, which leverage metaheuristic search algorithms (MSAs) to identify optimal feature subsets. In this paper, we propose a novel wrapper-based feature selection method utilizing the Triangulation Topology Aggregation Optimizer (TTAO), a newly developed algorithm inspired by the geometric properties of triangular topology and similarity. To adapt the TTAO for binary feature selection tasks, we introduce a conversion mechanism that transforms continuous decision variables into binary space, allowing the TTAO-which is inherently designed for real-valued problems-to function efficiently in binary domains. TTAO incorporates two distinct search strategies, generic aggregation and local aggregation, to maintain an effective balance between global exploration and local exploitation. Through extensive experimental evaluations on a wide range of benchmark datasets, TTAO demonstrates superior performance over conventional MSAs in feature selection tasks. The results highlight TTAO's capability to enhance model accuracy and computational efficiency, positioning it as a promising tool to advance feature selection and support industrial innovation in data-driven tasks.