Knowledge Calibration Fusion and Label Space Graph Regularization-Based Multicenter Fuzzy Systems

Traditional single-center learning algorithms often face significant limitations in handling heterogeneous data integration, including insufficient generalization ability, weak privacy protection, and difficulties adapting to multi-center scenarios. To address these challenges, multi-center learning has emerged as a critical technological framework. Although our previously proposed MKTC-R0T algorithm partially addressed the integration and modeling of multi-center data through the knowledge transfer calibration strategy, it still exhibits notable shortcomings in terms of knowledge fusion stability, model interpretability and generalization, as well as the utilization of complementary information across centers. To overcome these limitations, we propose a Knowledge Calibration Fusion and Label Space Graph Regularization-based Multi-center TSK Fuzzy System (KCF-LSG-MTSK). Specifically, we introduce an enhanced knowledge calibration and fusion strategy to effectively integrate heterogeneous information between the base center (BC) and auxiliary center (AC). We also propose a novel label space graph regularization scheme that constructs both intracenter and intercenter graph structures, leveraging data consistency and complementarity to enhance the quality of knowledge sharing. Furthermore, building upon firstorder TSK fuzzy system optimization, our approach incorporates a projected maximum mean discrepancy (PMMD) transfer term to effectively reduce data distribution discrepancies between the BC and AC. Experimental results on thirteen benchmark datasets demonstrate that KCFLSGMTSK achieves an average accuracy of 88.7%, significantly outperforming stateoftheart singlecenter and multicenter methods, thereby validating the superiority of our approach in heterogeneous data integration, knowledge transfer, and interpretable classification.