نوع مقاله : مقاله استخراج شده از پایان نامه
عنوان مقاله English
نویسندگان English
Breast cancer diagnosis requires accurate classification methods to support early detection and improve clinical outcomes. This study aims to propose a hybrid optimization framework to enhance Twin Support Vector Machine (Twin SVM) performance for breast cancer classification. The framework integrates four marine-inspired metaheuristic algorithms — Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Marine Predators Algorithm (MPA) — as cooperative structure. Q-Learning dynamically controls the exploration-exploitation balance by adaptively selecting the most effective search operator at each iteration, preventing premature convergence and improving global search capability. Experimental results on two benchmark breast cancer datasets demonstrate that the proposed method achieves superior accuracy, precision, and recall compared to conventional Twin SVM and single-algorithm optimizers.
کلیدواژهها English
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