Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors

• URL: http://arxiv.org/abs/2402.16388v3
• Date: Thu, 20 Feb 2025 13:28:41 GMT
• Title: Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors
• Authors: Oliver Hennhöfer, Christine Preisach,
• Abstract summary: In this work, we formally define and evaluate leave-one-out-, bootstrap-, and cross-conformal methods for anomaly detection.<n>We demonstrate that derived methods for calculating resampling-conformal $p$-values strike a practical compromise between statistical efficiency (full-conformal) and computational efficiency (split-conformal) as they make more efficient use of available data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The requirement of uncertainty quantification for anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates ($\alpha$) without compromising the statistical power ($1-\beta$) of these systems can build trust and reduce costs related to false discoveries. The field of conformal anomaly detection emerges as a promising approach for providing respective statistical guarantees by model calibration. However, the dependency on calibration data poses practical limitations - especially within low-data regimes. In this work, we formally define and evaluate leave-one-out-, bootstrap-, and cross-conformal methods for anomaly detection, incrementing on methods from the field of conformal prediction. Looking beyond the classical inductive conformal anomaly detection, we demonstrate that derived methods for calculating resampling-conformal $p$-values strike a practical compromise between statistical efficiency (full-conformal) and computational efficiency (split-conformal) as they make more efficient use of available data. We validate derived methods and quantify their improvements for a range of one-class classifiers and datasets.

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