A Doubly Cross-Interaction Deep TSK Fuzzy Classifier with Residual-Directed Dynamic Collaboration for Interpretable Dependencies

Although two-view Takagi-Sugeno-Kang (TSK) fuzzy classifiers have shown promising performance, most existing methods rely on static fusion and lack dynamic boundary modeling. Moreover, complex coupling architectures may obscure rule-level semantics and reduce interpretability. This study proposes a residual-directed two-view deep TSK fuzzy classifier (R-TSKFC), which establishes a hierarchical two-view collaborative learning framework to overcome the limitations of static fusion and enable adaptive modeling of shared decision boundaries. A two-view training strategy is proposed to implicitly enforce semantic consistency between views and suppress view-specific drift for the parameter space. In the feature space, a residual-directed mechanism explicitly conveys boundary-relevant information between views, allowing the proposed model to focus on under-learned regions and enhance discriminative capability. Extensive experiments on seven UCI benchmark datasets and seven real-world medical datasets demonstrate that R-TSKFC achieves competitive classification performance, with consistent improvements over representative baselines. The model also exhibits competitive performance in terms of classification accuracy, generalization ability, and computational efficiency, while preserving interpretability by relying on original input features. Moreover, the residual-directed mechanism provides an auxiliary perspective for understanding the decision process at each network layer, without compromising the transparency of the learned fuzzy rules.