Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection

Lorenzo Perini, Paul Christian Bürkner, Arto Klami

Tutkimustuotos: ArtikkelijulkaisuKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

Anomaly detection methods identify examples that do not follow the expected behaviour, typically in an unsupervised fashion, by assigning real-valued anomaly scores to the examples based on various heuristics. These scores need to be transformed into actual predictions by thresholding so that the proportion of examples marked as anomalies equals the expected proportion of anomalies, called contamination factor. Unfortunately, there are no good methods for estimating the contamination factor itself. We address this need from a Bayesian perspective, introducing a method for estimating the posterior distribution of the contamination factor for a given unlabeled dataset. We leverage several anomaly detectors to capture the basic notion of anomalousness and estimate the contamination using a specific mixture formulation. Empirically on 22 datasets, we show that the estimated distribution is well-calibrated and that setting the threshold using the posterior mean improves the detectors' performance over several alternative methods.

Alkuperäiskielienglanti
LehtiProceedings of Machine Learning Research
Vuosikerta202
Sivut27668-27679
Sivumäärä12
ISSN2640-3498
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Machine Learning - Honolulu, Yhdysvallat (USA)
Kesto: 23 heinäk. 202329 heinäk. 2023
Konferenssinumero: 40

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