Publication Details

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

authored by
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.

External Organisation(s)
Ludwig-Maximilians-Universität München (LMU)
Type
Conference contribution
Pages
392-405
No. of pages
14
Publication date
2023
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Theoretical Computer Science, General Computer Science
Electronic version(s)
https://doi.org/10.1007/978-3-031-30047-9_31 (Access: Closed)