Antecedent-Aligned Rule Transfer with Partial-Overlap Consequent Fusion for Multi-Center TSK Fuzzy Classification

Multi-center Takagi–Sugeno–Kang (TSK) fuzzy classification faces challenges arising from asymmetric sample sizes and partially overlapping local rule bases, as independently learned antecedents may not denote comparable fuzzy regions across centers. We propose MCRT-TSK, which establishes shared antecedent identities for semantic rule matching while allowing each center to retain only locally supported rules. At the target center, a sample-count-adaptive criterion combines source-side support with target activation evidence, and consequent blocks are fused only from sources containing the matched antecedent. A rule-level prior and a response-level anchor regularize a convex target consequent-estimation problem with a closed-form solution. On 18 UCI datasets, MCRT-TSK achieves the highest or tied-highest target-test ACC among methods with valid outputs on 16 of the 18 datasets. Across three stress-test families, shared antecedent transfer yields positive mean ACC, Macro-F1, and G-mean gains over local-target training; identity-preserving consequent transfer also yields positive mean margins over a within-order shuffled-prior control in all nine task-family–metric summaries. Statistical, ablation, efficiency, and rule-level evidence supports the full MCRT-TSK under the simulated-center protocol and aligned rule transfer in the stress tests.