Isolation Forest
Isolation Forest is a machine learning algorithm that belongs to the class of unsupervised outlier detection methods, distinguished by its use of random partitioning in tree ensembles to isolate anomalies based on the premise that outliers are few and dissimilar, and thus more susceptible to isolation with fewer splits.
347
Publications
24.4K
Citations
1.3K
Authors
551
Institutions
Publications per year
2017–2026
341
Variants
Iforest
347
Fei Tony Liu, Kai Ming Ting, Zhi‐Hua Zhou · 2008 · 5.6K citations
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang et al. · IEEE Transactions on Knowledge and Data Engineering · 2023 · 451 citations
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551

University of Padua
Padua, Italy
10 h-index
28 publications
1.4K citations
Nanjing University
Nanjing, China
8 h-index
12 publications
7.8K citations

Xidian University
Xi'an, China
7 h-index
24 publications
886 citations

Lublin University of Technology
Lublin, Poland
5 h-index
10 publications
715 citations
Jilin University
Changchun, China
5 h-index
13 publications
494 citations

Central South University
Changsha, China
5 h-index
24 publications
673 citations

Sejong University
Seoul, South Korea
5 h-index
6 publications
410 citations

Harbin Institute of Technology
Harbin, China
5 h-index
19 publications
508 citations
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21 publications · 1.6K citations
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15 publications · 656 citations
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Repository · 10 publications · 345 citations
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8 publications · 284 citations
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7 publications · 486 citations
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