用于带弃权（不执行）的可解释分类的证据规则学习

中文摘要 AI 辅助

可解释分类在实际部署中往往不仅需要准确的预测：模型应对其决策背后的证据保持透明，并在无法可靠决策时弃权（不执行）。我们提出Fast Evidential Rule Learning（FERL，快速证据规则学习），一种学习可解释、准确的模糊规则模型的方法，其输出具有证据性。与事后校准不同，FERL的置信度、似然度和弃权（不执行）能力直接来自模糊隶属度，通过单次确定性推理即可获得，无需辅助头、预留集或重复推理。我们的理论分析进一步表明，FERL具有Lipschitz稳定性，这意味着其证据输出随输入平滑变化。在30个表格数据集基准测试中，FERL在统计上显著优于最先进的规则学习器，平均准确率比次优结果高2.6%。其原生集预测在信度分类器中实现了最佳的效用折扣准确率（u₆₅/u₈₀=0.80/0.83，而朴素信度分类器为0.79/0.80），同时具有更高的集覆盖率（0.92，对比≤0.82）。FERL在表格近分布外（OOD）检测中与专用OOD检测器表现相当（最强基线的AUROC为77.4，FERL为77.7）。在检测器-类别不相交的概念瓶颈评估中，其在CUB和AwA2上与最强专用检测器的AUROC差距均在2.3以内，同时在AwA2上实现了最佳的AUPR-Out（68.3）和新类拒绝（57.2），并能指出哪些属性是异常的。

英文摘要

Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abstain when they cannot decide reliably. We introduce Fast Evidential Rule Learning (FERL), a method that learns interpretable, accurate fuzzy rule models whose outputs are evidential. Unlike post-hoc calibration, FERL's belief, plausibility, and abstention capabilities arise directly from the fuzzy memberships in a single deterministic pass, with no auxiliary head, held-out set, or repeated inference. Our theoretical analysis further shows that FERL is Lipschitz stable, which means that its evidential outputs vary smoothly with the input. Against state-of-the-art rule learners, FERL is statistically significantly more accurate across a 30 tabular-dataset benchmark ($+2.6\%$ average accuracy over the second best). Its native set predictions attain the best utility-discounted accuracy among credal classifiers ($u_{65}/u_{80}=0.80/0.83$ vs.\ $0.79/0.80$ for the naive credal classifier), at higher set coverage ($0.92$ vs.\ $\le0.82$). FERL also matches dedicated out-of-distribution detectors on tabular near-OOD detection ($77.7$ vs.\ $77.4$ AUROC for the strongest baseline). Under detector-class-disjoint concept-bottleneck evaluation, its it is within $2.3$ AUROC points of the strongest dedicated detector on both CUB and AwA2, while attaining the best AwA2 AUPR-Out ($68.3$) and novel-class rejection ($57.2$), while being able to name which attributes are anomalous.

发表机构

• School of Computer Science and Electronic Engineering（计算机科学与电子工程学院）
• University of Essex（埃塞克斯大学）

机构由 AI 辅助整理，请以论文原文为准。