用梯度下降训练紧凑规则模型，兼顾可解释性与高性能。
Compact Rule-Based Classifier Learning via Gradient Descent
- 基于模糊逻辑构建可微规则系统，支持用户约束复杂度
- 在40个数据集上比RIPPER高5%准确率，规则量少90%
- 仅用3%规则数达到顶尖模型96%性能，适合高风险决策场景
规则模型因透明性和可解释性，在高风险决策中至关重要，但其离散特性导致优化与扩展困难。本文提出模糊规则推理器（FRR），一种新型基于梯度的规则学习系统，可在严格控制规则复杂度的同时实现竞争力性能。为最大化可解释性，FRR采用语义明确的模糊逻辑划分，这是现有神经模糊方法无法实现的，并使用充分规则（单规则）决策，避免了加性规则集合的组合爆炸。在40个数据集上的广泛评估表明：(1) FRR平均准确率比传统规则方法（如RIPPER）高出5%；(2) 与树模型（如CART）相比，规则规模减少90%仍保持相当精度；(3) 仅需其规则基数的3%，即可达到当前最优加性规则模型96%的准确率。
原文摘要 · Abstract (English)
Rule-based models are essential for high-stakes decision-making due to their transparency and interpretability, but their discrete nature creates challenges for optimization and scalability. In this work, we present the Fuzzy Rule-based Reasoner (FRR), a novel gradient-based rule learning system that supports strict user constraints over rule-based complexity while achieving competitive performance. To maximize interpretability, the FRR uses semantically meaningful fuzzy logic partitions, unattainable with existing neuro-fuzzy approaches, and sufficient (single-rule) decision-making, which avoids the combinatorial complexity of additive rule ensembles. Through extensive evaluation across 40 datasets, FRR demonstrates: (1) superior performance to traditional rule-based methods (e.g., $5\%$ average accuracy over RIPPER); (2) comparable accuracy to tree-based models (e.g., CART) using rule bases $90\%$ more compact; and (3) achieves $96\%$ of the accuracy of state-of-the-art additive rule-based models while using only sufficient rules and requiring only $3\%$ of their rule base size.
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