An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient-Boosting and Fuzzy Rule-Based Models
Fuzzy rule models offer strong interpretability but suffer from poor scalability and susceptibility to overfitting in complex tasks and large-scale data scenarios. To address these limitations, this paper proposes a novel ensemble framework integrating gradient boosting with fuzzy rule-based base learners. We introduce a dynamic control factor that adaptively adjusts the weights of fuzzy base models in each boosting iteration, simultaneously serving as a regularizer and performance optimizer. Additionally, we design a validation-set-driven, sample-level correction mechanism to enhance generalization and ensemble diversity. Experimental results demonstrate that our approach significantly mitigates overfitting, reduces rule complexity (e.g., fewer rules and shorter antecedents), and preserves high model interpretability and maintainability. The method thus provides a practical pathway for deploying interpretable AI in complex industrial applications.