Monash University and

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 paper proposes a method called Isolation Forest (iForest) which detects anomalies purely based on the concept of isolation without empl...

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Bibliographic Details
Main Authors: Fei Tony Liu, Kai Ming Ting, Zhi-hua Zhou
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Categories and Subject Descriptors
H.2.8 [Database Management
Database Applications— Data Mining
I.2.6 [Artificial Intelligence
Learning General Terms
Algorithm
Design
Experimentation Additional Key Words and Phrases
Anomaly detection
outlier detection
ensemble methods
binary tree
Orca
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.673.5779
http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/tkdd11.pdf
Description
Summary: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 paper 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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