Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems

• URL: http://arxiv.org/abs/2602.19253v1
• Date: Sun, 22 Feb 2026 16:08:06 GMT
• Title: Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems
• Abstract summary: Fuzzy objective systems show strong potential in explainable AI due to their rule-based architecture linguistic variables.<n>X-ANFIS achieves while maintaining competitive accuracy, recovering solutions beyond the MOO front.
• Score: 2.761009930426063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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