TITLE: Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets 

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 based on metrics that can be computed with
normal data only. First promising results can be obtained considering the
hypervolume and the false positive rate as meta-features.
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 based on metrics that can be computed with
normal data only. First promising results can be obtained considering the
hypervolume and the false positive rate as meta-features.
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
http://arxiv.org/abs/2211.13681v1