# Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library

arXiv: https://arxiv.org/abs/2607.20277 · HTML: https://mlsanity.com/paper/2607.20277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques](https://mlsanity.com/paper/2308.15284/) (2023)
- [A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.12571/) (2022)
- [A Gentle Introduction and Survey on Computing with Words (CWW) Methodologies](https://mlsanity.com/paper/2208.06532/) (2022)
- [Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results](https://mlsanity.com/paper/2110.14845/) (2021)
- [Reliable Classification with Conformal Learning and Interval-Type 2 Fuzzy Sets](https://mlsanity.com/paper/2504.15360/) (2025)

## Abstract (arXiv)

Machine learning models achieve high predictive accuracy in regression tasks, but their deployment in safety-critical and regulated domains requires interpretability. While fuzzy rule-based systems offer transparent, linguistically explicit interpretable models, Mamdani-style fuzzy regression remains underrepresented in modern machine learning software libraries. This paper presents an interpretable regression extension for the Ex-Fuzzy library, enabling Mamdani fuzzy inference with scalar consequents learned directly from data. For this, a target-aware partition initialisation strategy based on Fuzzy C-Means clustering is introduced, in which linguistic variables are derived from an augmented input-output space to emphasise output-relevant regions of the feature space. The proposed extension is evaluated on ten regression datasets from the KEEL repository, comparing Gaussian and trapezoidal partition strategies against standard baselines including linear regression, multilayer perceptron, and random forests. Experimental results show that Gaussian partitions consistently outperform uniform trapezoidal partitions, achieving a mean coefficient of determination of approximately 0.86 while producing compact rule bases of 10-15 human-readable rules. The proposed implementation provides a transparent and competitive alternative to black-box regression models, supporting practical interpretability with competitive predictive performance.