Abstract
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 (u65/u80=0.80/0.83 vs.\ 0.79/0.80 for the naive credal classifier), at higher set coverage (0.92 vs.\ ≤0.82). 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.
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