Akademik Veri Yönetim Sistemi
Signal, Image and Video Processing, cilt.19, sa.13, 2025 (SCI-Expanded, Scopus)
This study proposes a dual-stage optimization framework for uncertainty-aware classification by integrating the Intuitionistic Fuzzy Any Relation Clustering Algorithm (IF-ARCA) with Intuitionistic Fuzzy K-Nearest Neighbors (IF-KNN). In the first stage, Harris Hawks Optimization (HHO) calibrates IF-ARCA parameters to construct reliable membership and non-membership matrices, while in the second stage HHO independently tunes IF-KNN parameters, ensuring decoupled and stable convergence. HHO was chosen for its effective exploration–exploitation balance in high-dimensional search spaces, and the dual-stage design uniquely enables clustering and classification to be optimized without mutual interference. Extensive experiments on eight benchmark datasets (seven from UCI, plus Yeast and Credit Fraud for scalability) confirm the superiority of the proposed approach: the fuzzy metric variant achieved F1 = 0.993 on Credit Fraud and 0.946 on MONK’s Problems, while cosine similarity reached 0.989 on Digits. Compared with established FKNN variants, the framework yielded 20–35% relative improvements and demonstrated statistically significant gains on challenging datasets (Iris, MONK’s, Yeast; Wilcoxon p < 0.05). These results highlight the framework’s robustness under class overlap and imbalance, while maintaining competitive performance in high-dimensional domains, establishing a novel contribution to clustering-guided classification and nature-inspired optimization.