Title: L-FMLC: End-to-End Neuro-Fuzzy Learning for Adaptive and Scalable Interpretability
Primary area: interpretability and explainable AI
Keywords: Deep Learning, Fuzzy Logic, Neuro-Fuzzy Systems, Hybrid Models, Interpretability, Explainable AI (XAI), TSK Fuzzy Systems, Large Language Model, Generative AI, Rule Extraction, Time Series Forecasting, Regression, Classification, Model Efficiency.

Abstract: Deep learning models offer remarkable performance but their black-box nature remains a key barrier to deployment in high-stakes domains. Neuro-fuzzy systems provide a promising path towards inherent interpretability but often struggle with adaptability and scalability. Our prior work, FMLC, coupled a deep model with a Type-I TSK-style output layer but relied on static, pre-defined fuzzy sets and suffered from rule explosion on complex problems. This paper introduces L-FMLC, a framework with a fully adaptive fuzzification layer that addresses these limitations. The layer learns the optimal positions and shapes of its membership functions and autonomously determines the required number of fuzzy sets for each feature via a novel, regularized gating mechanism with an explicit pruning policy. This structural learning is guided by interpretability-driven regularizers that ensure the resulting fuzzy partitions are parsimonious and semantically coherent. To manage the subsequent rule base, we introduce a two-stage distillation framework that combines architectural pruning with hierarchical clustering to condense thousands of rules into a compact, high-fidelity set of ``meta-rules". We demonstrate that these structured rules can be translated into natural language explanations using a large language model. Experiments against strong interpretable baselines like EBM and XGBoost show that our regularized L-FMLC framework achieves competitive or superior predictive accuracy. Crucially, ablation studies provide compelling empirical evidence that this regularization is essential not just for interpretability, but also for generalization.