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Generalized isolation forest for anomaly detection

Lesouple, Julien and Baudoin, Cédric and Spigai, Marc and Tourneret, Jean-Yves Generalized isolation forest for anomaly detection. (2021) Pattern Recognition Letters, 149. 109-119. ISSN 0167-8655

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Official URL: https://doi.org/10.1016/j.patrec.2021.05.022

Abstract

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.

Item Type:Article
HAL Id:hal-03382634
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Thales (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Laboratoire de recherche en télécommunications spatiales et aéronautiques - TéSA (FRANCE)
Laboratory name:
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Deposited On:19 Jul 2021 08:29

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