AI 中文总结
研究针对安全关键等领域机器学习模型需可解释性的问题，为Ex-Fuzzy库提出回归扩展，采用基于模糊C均值聚类的目标感知分区初始化策略，在十个数据集上评估，高斯分区表现优，提供了透明且有竞争力的黑盒回归替代方案。
AI 中文摘要
机器学习模型在回归任务中能实现高预测精度，但在安全关键和受监管领域的部署需要可解释性。基于模糊规则的系统提供了透明、语言明确的可解释模型，然而Mamdani风格的模糊回归在现代机器学习软件库中仍未得到充分体现。本文为Ex-Fuzzy库提出了一种可解释的回归扩展，实现了从数据中直接学习标量结果的Mamdani模糊推理。为此，引入了基于模糊C均值聚类的目标感知分区初始化策略，从增强的输入输出空间导出语言变量以强调特征空间中与输出相关的区域。在KEEL存储库的十个回归数据集上对该扩展进行评估，将高斯和梯形分区策略与包括线性回归、多层感知器和随机森林在内的标准基线进行比较。实验结果表明，高斯分区始终优于均匀梯形分区，平均决定系数约为0.86，同时生成10 - 15条人类可读规则的紧凑规则库。该实现为黑盒回归模型提供了一个透明且有竞争力的替代方案，支持具有竞争力预测性能的实际可解释性。
英文摘要
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.
Journal refIEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE World Congress on Computational Intelligence (WCCI), 2026
