How to pick the best anomaly detector?
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912847927181312 |
|---|---|
| author | Hein, Marie Kasieczka, Gregor Krämer, Michael Moureaux, Louis Mück, Alexander Shih, David |
| author_facet | Hein, Marie Kasieczka, Gregor Krämer, Michael Moureaux, Louis Mück, Alexander Shih, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14832 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How to pick the best anomaly detector? Hein, Marie Kasieczka, Gregor Krämer, Michael Moureaux, Louis Mück, Alexander Shih, David High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability 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. |
| title | How to pick the best anomaly detector? |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2511.14832 |