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Isolation Forest is an algorithm for data anomaly detection using binary trees. It was developed by Fei Tony Liu in 2008.[1] It has a linear time complexity and a low memory use, which works well for high-volume data.[2][3] It is based on the assumption that because anomalies are few and different from other data, they can be isolated using few partitions. Like decision tree algorithms, it does not perform density estimation. Unlike decision tree algorithms, it uses only path length to output an anomaly score, and does not use leaf node statistics of class distribution or target value.
Isolation Forest is fast because it splits the data space, randomly selecting an attribute and split point. The anomaly score is inversely associated with the path-length because anomalies need fewer splits to be isolated, because they are few and different.
- ^ Liu, Fei Tony. "First Isolation Forest implementation on Sourceforge".
- ^ Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (December 2008). "Isolation Forest". 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422. doi:10.1109/ICDM.2008.17. ISBN 978-0-7695-3502-9. S2CID 6505449.
- ^ Liu, Fei Tony; Ting, Kai Ming; Zhou, Zhi-Hua (December 2008). "Isolation-Based Anomaly Detection". ACM Transactions on Knowledge Discovery from Data. 6: 3:1–3:39. doi:10.1145/2133360.2133363. S2CID 207193045.