见证者解释异常值

中文摘要 AI 辅助

无监督异常检测会在单次遍历中对带噪声的未标记样本的每个点进行评分，且越来越需要解释某点被标记的原因。然而主流检测器仅给出评分，未说明驱动评分的特征，解释则通过事后添加的SHAP或LIME方法实现，这些方法对每个点需重新查询检测器数千次，且仅能近似解释。我们提出WAND，一种天生可解释的无监督表格异常检测器。WAND围绕单位球上的方向组织计算，通过某点投影偏离亚高斯极值基线的程度对其评分。该方法的创新在于，标记某点的见证方向是特征空间中的向量，构成该点的逐特征归因，此归因在评分时即可无成本获得，且因评分可微，可通过梯度恢复。评分时间与样本量呈线性关系，探针效率边界保证每个异常值都有见证方向，即对应解释。在47个ADBench数据集上，WAND在与16种无监督基线方法ROC-AUC持平的情况下，获得最佳平均Friedman秩，因此其优势在于无需牺牲精度即可获得可解释性；其原生解释比事后的SHAP/LIME和ECOD更准确、更忠实，查询成本却仅为后者的一小部分。因此，WAND是可解释异常检测的实用、可解释解决方案。

英文摘要

Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and increasingly must also explain why a point is flagged. Yet the dominant detectors give a score with no account of which features drive it, and explanations are bolted on post-hoc with SHAP or LIME, which re-query the detector thousands of times per point and only approximate it. We introduce WAND, an unsupervised tabular anomaly detector that is explainable by design. WAND organises its computation around directions on the unit sphere, scoring each point by how far its projection escapes a sub-Gaussian extreme-value baseline. The originality of our approach is that the witness directions that flag a point, being vectors in feature space, are its explanation, a per-feature attribution obtained at no cost over scoring and, since the score is differentiable, recoverable by gradients. Scoring is linear in the sample size, and a probe-efficiency bound guarantees every anomaly a witness, hence an explanation. Across 47 ADBench datasets WAND attains the best mean Friedman rank at ROC-AUC parity with 16 unsupervised baselines, so the gain is interpretability at no accuracy cost; its native explanations are more accurate and faithful than post-hoc SHAP/LIME and ECOD at a fraction of the query cost. WAND is thus a practical, interpretable solution for explainable anomaly detection.

发表机构

• EPITA Research Laboratory（EPITA研究实验室）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted at ICDM 2026