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

Authors

1.3K

  1. GA
    Gian Antonio Susto

    University of Padua, Italy

    10 publications · 478 citations

    10

    h-index

  2. PK
    Paweł Karczmarek

    University of Alberta, Canada

    5 publications · 348 citations

    5

    h-index

  3. HX
    Haolong Xiang

    Nanjing University, China

    4 publications · 80 citations

    4

    h-index

  4. KM
    Kai Ming Ting

    Monash University, Australia

    4 publications · 7.6K citations

    4

    h-index

  5. XZ
    Xuyun Zhang

    Nanjing University, China

    4 publications · 80 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. IEEE Access

    21 publications · 1.6K citations

    19

    h-index

  2. Sensors

    15 publications · 656 citations

    14

    h-index

  3. arXiv (Cornell University)

    Repository · 10 publications · 345 citations

    10

    h-index

  4. Energies

    8 publications · 284 citations

    8

    h-index

  5. 7

    h-index

1–5