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Schubert, David; Gupta, Pritha ORCID logoORCID: https://orcid.org/0000-0002-7277-4633 and Wever, Marcel ORCID logoORCID: https://orcid.org/0000-0001-9782-6818 (April 2023): Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets. Advances in Intelligent Data Analysis XXI, Louvain-la-Neuve, Belgium, 12-14 April 2023. Crémilleux, Bruno; Hess, Sibylle and Nijssen, Siegfried (eds.) : Cham: Springer Nature Switzerland. pp. 392-405

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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.

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