KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning

2026-02-03

AI Summary

This work proposes KANFIS, a neuro-symbolic architecture that integrates fuzzy inference with additive functional decomposition to overcome the exponential rule explosion inherent in traditional ANFIS when operating in high-dimensional spaces. By replacing product-based inference with an additive aggregation mechanism, KANFIS reduces rule complexity from exponential to linear growth while supporting both Type-1 and Interval Type-2 fuzzy systems to effectively model uncertainty. Structured rule sets are generated through sparse masking, endowing the model with intrinsic interpretability and uncertainty awareness. Experimental results demonstrate that KANFIS achieves performance comparable to state-of-the-art neural networks and neuro-fuzzy models across multiple benchmark tasks, yet maintains concise, semantically clear rules and transparent reasoning processes.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to combine the learning capabilities of neural network with the reasoning transparency of fuzzy logic. However, conventional ANFIS architectures suffer from structural complexity, where the product-based inference mechanism causes an exponential explosion of rules in high-dimensional spaces. We herein propose the Kolmogorov-Arnold Neuro-Fuzzy Inference System (KANFIS), a compact neuro-symbolic architecture that unifies fuzzy reasoning with additive function decomposition. KANFIS employs an additive aggregation mechanism, under which both model parameters and rule complexity scale linearly with input dimensionality rather than exponentially. Furthermore, KANFIS is compatible with both Type-1 (T1) and Interval Type-2 (IT2) fuzzy logic systems, enabling explicit modeling of uncertainty and ambiguity in fuzzy representations. By using sparse masking mechanisms, KANFIS generates compact and structured rule sets, resulting in an intrinsically interpretable model with clear rule semantics and transparent inference processes. Empirical results demonstrate that KANFIS achieves competitive performance against representative neural and neuro-fuzzy baselines.

Problem

• ANFIS
• rule explosion
• interpretability
• uncertainty modeling
• high-dimensional complexity

Innovation

• KANFIS
• additive decomposition
• neuro-symbolic
• interpretable AI
• uncertainty-aware