Generalized isolation forest for anomaly detection

•We propose a new unsupervised anomaly detection (AD) algorithm.•This algorithm is based on isolation forest with random hyperplanes instead of random dimensions.•The proposed method improves the existing extended isolation forest (EIF) in terms of computation time. This letter introduces a generali...

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Bibliographic Details
Published in:Pattern recognition letters 2021-09, Vol.149, p.109-119
Main Authors: Lesouple, Julien, Baudoin, Cédric, Spigai, Marc, Tourneret, Jean-Yves
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Anomaly detection
Branches
Computer Science
Isolation forest
Signal and Image Processing
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Description
Summary:•We propose a new unsupervised anomaly detection (AD) algorithm.•This algorithm is based on isolation forest with random hyperplanes instead of random dimensions.•The proposed method improves the existing extended isolation forest (EIF) in terms of computation time. This letter introduces a generalization of Isolation Forest (IF) based on the existing Extended IF (EIF). EIF has shown some interest compared to IF being for instance more robust to some artefacts. However, some information can be lost when computing the EIF trees since the sampled threshold might lead to empty branches. This letter introduces a generalized isolation forest algorithm called Generalized IF (GIF) to overcome these issues. GIF is faster than EIF with a similar performance, as shown in several simulation results associated with reference databases used for anomaly detection.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2021.05.022