- Title
- Isolation-based anomaly detection
- Creator
- Liu, Fei; Ting, Kaiming; Zhou, Zhi-Hua
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34102
- Identifier
- vital:6369
- Identifier
-
https://doi.org/10.1145/2133360.2133363
- Identifier
- ISSN:1556-4681
- Abstract
- 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
- Relation
- ACM Transactions on Knowledge Discovery from Data Vol. 6, no. 1 (2012), p. 1-39
- Rights
- Copyright Association for Computing Machinery, Inc.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Information systems; Information systems applications; Data mining; Computing methodologies; Machine learning
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