The need for uncertainty quantification in anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates without inflating Type II error rates in these systems can build trust and reduce costs associated with false discoveries. The field of conformal anomaly detection emerges as a promising approach for providing respective statistical and finite-sample validity guarantees through model calibration. However, reliance on calibration data imposes practical limitations, especially in low-data regimes. In this work, we formally define and evaluate leave-one-out-, bootstrap-, and cross-conformal methods for conformal anomaly detection, building on methods from the field of conformal prediction. Looking beyond the classical split-conformal approach, we show that derived methods for calculating resampling-conformal $p$-values offer a practical compromise between the data efficiency of full-conformal (transductive) approaches and the computational efficiency of split-conformal (inductive) methods. We validate derived methods and quantify their improvements for a range of one-class classifiers and datasets.

翻译：在异常检测系统中，不确定性量化的需求日益重要。在此背景下，有效控制系统中的第一类错误率而不增加第二类错误率，有助于建立信任并降低误判相关的成本。共形异常检测领域通过模型校准提供统计和有限样本有效性保证，成为一种有前景的方法。然而，对校准数据的依赖带来了实际限制，尤其是在低数据场景下。本研究基于共形预测领域的方法，正式定义并评估了用于共形异常检测的留一法、自助法和交叉共形方法。超越经典的分裂共形方法，我们展示了计算重抽样共形$p$值的衍生方法在全共形（直推式）方法的数据效率与分裂共形（归纳式）方法的计算效率之间提供了实用折衷。我们验证了这些衍生方法，并量化了它们在多种一类分类器和数据集上的改进效果。