Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918349764558848 |
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| author | Khaled, Qusai Kaymak, Uzay Genga, Laura |
| author_facet | Khaled, Qusai Kaymak, Uzay Genga, Laura |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems Khaled, Qusai Kaymak, Uzay Genga, Laura Machine Learning Neural and Evolutionary Computing 90C29, 68T05 I.2.6; I.2.3 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. |
| title | Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems |
| topic | Machine Learning Neural and Evolutionary Computing 90C29, 68T05 I.2.6; I.2.3 |
| url | https://arxiv.org/abs/2602.19253 |