Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
ACM transactions on knowledge discovery from data, 2012-03, Vol.6 (1), p.1-39
Erscheinungsjahr
2012
Quelle
ACM Digital Library
Beschreibungen/Notizen
Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called
isolation
. This article proposes a method called Isolation Forest (
i
Forest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods.
As a result,
i
Forest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that
i
Forest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects.
i
Forest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.