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

arXiv: https://arxiv.org/abs/2511.08077 · HTML: https://mlsanity.com/paper/2511.08077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 10 of 10)

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- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)](https://mlsanity.com/paper/1903.10951/) (2019)
- [A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.12571/) (2022)
- [Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task](https://mlsanity.com/paper/2010.02006/) (2020)
- [FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief](https://mlsanity.com/paper/2012.00060/) (2020)
- [Discovering Categorical Main and Interaction Effects Based on Association Rule Mining](https://mlsanity.com/paper/2104.04728/) (2021)

## Abstract (arXiv)

The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalability issues with large datasets. The fusion of different techniques and strategies, particularly Gradient Boosting, with Fuzzy Rule-Based Models offers a robust solution to these challenges. This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability. At each iteration, a Fuzzy Rule-Based Model is constructed and controlled by a dynamic factor to optimize its contribution to the overall ensemble. This control factor serves multiple purposes: it prevents model dominance, encourages diversity, acts as a regularization parameter, and provides a mechanism for dynamic tuning based on model performance, thus mitigating the risk of overfitting. Additionally, the framework incorporates a sample-based correction mechanism that allows for adaptive adjustments based on feedback from a validation set. Experimental results substantiate the efficacy of the presented gradient boosting framework for fuzzy rule-based models, demonstrating performance enhancement, especially in terms of mitigating overfitting and complexity typically associated with many rules. By leveraging an optimal factor to govern the contribution of each model, the framework improves performance, maintains interpretability, and simplifies the maintenance and update of the models.