An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models

The publication of the paper titled 'An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models' marks a notable advancement in machine learning. This framework aims to merge the strengths of Gradient Boosting and Fuzzy Rule-Based Models, addressing the inherent challenges of fuzzy models, including their complex design specifications and scalability issues with large datasets. By introducing a dynamic control factor, the framework optimizes the contributions of fuzzy models within the ensemble, preventing model dominance and encouraging diversity. This mechanism also acts as a regularization parameter and allows for adaptive adjustments based on performance feedback, significantly mitigating the risk of overfitting. Experimental results substantiate the efficacy of this integrated approach, demonstrating enhanced performance in ensemble learning. The implications of this research are profound, as it not only improves the interpretabil…