想要可解释的回归模型？这篇论文展示了Ex-Fuzzy库的新能力：用10-15条规则就能达到0.86的R²，比黑箱模型还直观。
该论文为Ex-Fuzzy库实现了Mamdani风格的可解释模糊回归扩展，利用模糊C均值聚类提出目标感知的变量初始化策略。在KEEL仓库的10个回归数据集上，高斯隶属度函数相比均匀梯形函数表现更优，平均决定系数约0.86，生成的规则库仅10-15条人类可读规则。该方法为黑箱回归模型提供了透明且性能竞争的替代方案。
Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library
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.