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Isolation Forest is an algorithm for data anomaly detection initially developed by Fei Tony Liu in 2008.[1] Isolation Forest detects anomalies using binary trees. The algorithm has a linear time complexity and a low memory requirement, which works well with high-volume data.[2][3]
In essence, the algorithm relies upon the characteristics of anomalies, i.e., being few and different, in order to detect anomalies. No density estimation is performed in the algorithm. The algorithm is different from decision tree algorithms in that only the path-length measure or approximation is being used to generate the anomaly score, no leaf node statistics on class distribution or target value is needed.
Isolation Forest is fast because it splits the data space randomly, using randomly selected attribute and randomly selected split point. The anomaly score is invertedly associated with the path-length as anomalies need fewer splits to be isolated, due to the fact that 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.
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