FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

Data Min. Knowl. Discov., pp. 109-133, 2012.

Cited by: 49|Bibtex|Views22|DOI:https://doi.org/10.1007/s10618-011-0234-x
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Other Links: pubmed.ncbi.nlm.nih.gov|dblp.uni-trier.de|dl.acm.org|link.springer.com

Abstract:

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task hav...More

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