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

TL;DR

KANFIS introduces a compact, interpretable neuro-symbolic model that unifies fuzzy reasoning with additive decomposition, effectively managing complexity and uncertainty in high-dimensional learning tasks.

Contribution

It proposes KANFIS, a novel neuro-fuzzy architecture that reduces rule complexity from exponential to linear and incorporates uncertainty modeling with Type-1 and Type-2 fuzzy logic.

Findings

KANFIS achieves competitive accuracy with fewer rules.

The model maintains interpretability through structured rule sets.

It effectively models uncertainty using fuzzy logic extensions.

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.