# Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems

arXiv: https://arxiv.org/abs/2602.19253 · HTML: https://mlsanity.com/paper/2602.19253/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 22 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)](https://mlsanity.com/paper/1903.10951/) (2019)
- [PyTSK: A Python Toolbox for TSK Fuzzy Systems](https://mlsanity.com/paper/2206.03310/) (2022)
- [Interpretable Fuzzy Systems For Forward Osmosis Desalination](https://mlsanity.com/paper/2602.08050/) (2026)

## Abstract (arXiv)

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.