IT2-ENFIS: Interval Type-2 Exclusionary Neuro-Fuzzy Inference System, an Attempt Toward Trustworthy Regression Learning
Chuan Xue, Jianli Gao, Zhou Gu · IEEE Transactions on Artificial Intelligence · 2025
As machine learning technologies progress and are increasingly applied to critical and sensitive fields, the reliability issues of earlier technologies are becoming more evident. For the new generation of machine learning solutions, trustworthiness frequently takes precedence over performance when evaluating their applicability for specific applications. This manuscript introduces the IT2-ENFIS neuro-fuzzy model, a robust and trustworthy single-network solution specifically designed for data regression tasks affected by substantial label noise and outliers. The primary architecture applies interval type-2 fuzzy logic and the Sugeno inference engine. A meta-heuristic gradient-based optimizer (GBO), the Huber loss function, and the Cauchy M-estimator are employed for robust learning. IT2-ENFIS demonstrates superior performance on noise-contaminated datasets and excels in real-world scenarios, with excellent generalization capability and interpretability.