Generalized isolation forest for anomaly detection

被引：77

作者：

Lesouple, Julien ^{[1
]}

Baudoin, Cedric ^{[2
]}

Spigai, Marc ^{[2
]}

Tourneret, Jean-Yves ^{[1
,3
]}

机构：

[1] TeSA, 7 Blvd Gare, F-31000 Toulouse, France

[2] Thales Alenia Space, 26 Ave Jean Francois Champollion, F-31100 Toulouse, France

[3] Univ Toulouse, INP ENSEEIHT IRIT, 2 Rue Charles Camichel, F-31071 Toulouse, France

来源：

PATTERN RECOGNITION LETTERS | 2021年 / 149卷

关键词：

Anomaly detection; Isolation forest; DENSITY; SUPPORT;

D O I：

10.1016/j.patrec.2021.05.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2021 Elsevier B.V. All rights reserved.

引用

页码：109 / 119

页数：11

共 15 条

[1]

[Anonymous], UNSUPERVISED ANOMALY

[2]

Brause R., 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence, P103, DOI 10.1109/TAI.1999.809773

[3] LOF: Identifying density-based local outliers [J].