Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets.

verfasst von

David Schubert, Pritha Gupta, Marcel Wever

Abstract

In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores using the metrics that can be computed with normal data only and order anomaly detectors using the predicted scores for selection. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.

Externe Organisation(en)

Ludwig-Maximilians-Universität München (LMU)

Typ

Aufsatz in Konferenzband

Seiten

392-405

Anzahl der Seiten

14

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik, Informatik (insg.)

Elektronische Version(en)

https://doi.org/10.1007/978-3-031-30047-9_31 (Zugang: Geschlossen)