Ensemble Outlier Detection

Ensemble Outlier Detection is an outlier detection paradigm that combines multiple base outlier detection models to improve detection accuracy and robustness by leveraging the strengths of diverse individual detectors through aggregation strategies.

109

Publications

5.9K

Citations

358

Authors

183

Institutions

Publications per year

2017–2026

83

Authors

358

  1. AZ
    Arthur Zimek

    Ludwig-Maximilians-Universität München, Germany

    6 publications · 959 citations

    6

    h-index

  2. BW
    Biao Wang

    Northeastern University, China

    4 publications · 136 citations

    4

    h-index

  3. RJ
    Ricardo J. G. B. Campello

    Universidade de São Paulo, Brazil

    4 publications · 529 citations

    4

    h-index

  4. JS
    Jörg Sander

    University of Alberta, Canada

    4 publications · 529 citations

    4

    h-index

  5. ZM
    Zhizhong Mao

    Northeastern University, China

    4 publications · 136 citations

    4

    h-index

1–5 of 358

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 175 citations

    7

    h-index

  2. Expert Systems with Applications

    6 publications · 252 citations

    6

    h-index

  3. Information Fusion

    3 publications · 145 citations

    3

    h-index

  4. Knowledge-Based Systems

    3 publications · 210 citations

    3

    h-index

  5. IEEE Access

    3 publications · 596 citations

    3

    h-index

1–5