用快速树结构生成可解释的模糊规则，兼顾效率与清晰性。
A Fast Interpretable Fuzzy Tree Learner
- 将经典决策树分裂算法改进为模糊树，实现高效构建
- 在多个数据集上达到顶尖分类精度，速度远超进化算法
- 适合需要快速可解释模型的工业场景
模糊规则系统因语言规则可解释而广泛用于可解释决策。但现有模糊规则挖掘算法难以同时保证合理的语言划分和小规模规则库。进化方法虽质量高，但计算成本过高；神经网络方法如ANFIS则难以保持语言可解释性。本文提出将经典树结构分裂算法适配至模糊树，结合贪婪算法的计算效率与模糊逻辑的可解释优势。该方法实现了可解释的语言划分，运行时间显著优于基于进化的方案，同时保持了竞争力的预测性能。在多个表格分类基准上的实验表明，本方法在准确率上可媲美最先进模糊分类器，但计算成本大幅降低，并生成复杂度受控的更可解释规则集。代码已公开：https://github.com/Fuminides/fuzzy_greedy_tree_public
原文摘要 · Abstract (English)
Fuzzy rule-based systems have been mostly used in interpretable decision-making because of their interpretable linguistic rules. However, interpretability requires both sensible linguistic partitions and small rule-base sizes, which are not guaranteed by many existing fuzzy rule-mining algorithms. Evolutionary approaches can produce high-quality models but suffer from prohibitive computational costs, while neural-based methods like ANFIS have problems retaining linguistic interpretations. In this work, we propose an adaptation of classical tree-based splitting algorithms from crisp rules to fuzzy trees, combining the computational efficiency of greedy algoritms with the interpretability advantages of fuzzy logic. This approach achieves interpretable linguistic partitions and substantially improves running time compared to evolutionary-based approaches while maintaining competitive predictive performance. Our experiments on tabular classification benchmarks proof that our method achieves comparable accuracy to state-of-the-art fuzzy classifiers with significantly lower computational cost and produces more interpretable rule bases with constrained complexity. Code is available in: https://github.com/Fuminides/fuzzy_greedy_tree_public
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