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arXiv · 2602.19253

Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems

Qusai Khaled·Uzay Kaymak·Laura Genga
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Abstract

Fuzzy systems show strong potential in explainable AI due to their rule-based architecture and linguistic variables. Existing approaches navigate the accuracy-explainability trade-off either through evolutionary multi-objective optimization (MOO), which is computationally expensive, or gradient-based scalarization, which cannot recover non-convex Pareto regions. We propose X-ANFIS, an alternating bi-objective gradient-based optimization scheme for explainable adaptive neuro-fuzzy inference systems. Cauchy membership functions are used for stable training under semantically controlled initializations, and a differentiable explainability objective is introduced and decoupled from the performance objective through alternating gradient passes. Validated in approximately 5,000 experiments on nine UCI regression datasets, X-ANFIS consistently achieves target distinguishability while maintaining competitive predictive accuracy, recovering solutions beyond the convex hull of the MOO Pareto front.

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Qusai Khaled, Uzay Kaymak, Laura Genga. 2026-02-22. Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems. https://doi.org/10.1109/cai68641.2026.11536320

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