Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values

• URL: http://arxiv.org/abs/2402.16388v2
• Date: Sat, 2 Mar 2024 13:40:04 GMT
• Title: Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values
• Authors: Oliver Hennh\"ofer and Christine Preisach
• Abstract summary: This work introduces a novel framework for anomaly detection, termed cross-conformal anomaly detection. We show that the derived methods for calculating cross-conformal $p$-values strike a practical compromise between statistical efficiency (full-conformal) and computational efficiency (split-conformal) for uncertainty-quantified anomaly detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the growing significance of reliable, trustworthy, and explainable machine learning, 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, particularly when follow-up procedures are expensive. Leveraging the principles of conformal prediction emerges as a promising approach for providing respective statistical guarantees by calibrating a model's uncertainty. This work introduces a novel framework for anomaly detection, termed cross-conformal anomaly detection, building upon well-known cross-conformal methods designed for prediction tasks. With that, it addresses a natural research gap by extending previous works in the context of inductive conformal anomaly detection, relying on the split-conformal approach for model calibration. Drawing on insights from conformal prediction, we demonstrate that the derived methods for calculating cross-conformal $p$-values strike a practical compromise between statistical efficiency (full-conformal) and computational efficiency (split-conformal) for uncertainty-quantified anomaly detection on benchmark datasets.

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