How to pick the best anomaly detector?
This addresses a neglected challenge in anomaly detection for researchers and practitioners, though it is incremental as it builds on existing weakly-supervised methods.
The paper tackles the problem of selecting the best anomaly detector in a model-agnostic way by introducing the ARGOS metric, which is shown to outperform existing metrics like binary cross-entropy loss in selecting sensitive models for anomaly detection.
Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic way is an important challenge which has hitherto largely been neglected. In this paper, we introduce the data-driven ARGOS metric, which has a sound theoretical foundation and is empirically shown to robustly select the most sensitive anomaly detection model given the data. Focusing on weakly-supervised, classifier-based anomaly detection methods, we show that the ARGOS metric outperforms other model selection metrics previously used in the literature, in particular the binary cross-entropy loss. We explore several realistic applications, including hyperparameter tuning as well as architecture and feature selection, and in all cases we demonstrate that ARGOS is robust to the noisy conditions of anomaly detection.