Journal
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
Volume 6, Issue 1, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2133360.2133363
Keywords
Anomaly detection; outlier detection; ensemble methods; binary tree; random tree ensemble; isolation; isolation forest
Funding
- Australian Postgraduate Awards (APA)
- Information and Communications Technologies (ICT) Postgraduate Research Scholarships
- National Science Foundation of China [61073097, 61021062]
- National Fundamental Research Program of China [2010CB327903]
- Jiangsu Science Foundation [BK2008018]
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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 (iForest), 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, iForest 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 iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.
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