Supervised Outlier Detection

Supervised Outlier Detection is a type of outlier detection method that belongs to the class of supervised learning approaches, distinguished by its reliance on labeled training data containing both normal and anomalous instances to learn a model capable of classifying new instances as either normal or outliers.

31

Publications

1.3K

Citations

97

Authors

53

Institutions

Publications per year

2017–2026

23

Authors

97

  1. AM
    Asad Masood Khattak

    Zayed University, United Arab Emirates

    2 publications · 31 citations

    2

    h-index

  2. JV
    Juha Valimaki

    1 publications · 30 citations

    1

    h-index

  3. SM
    Syed Mohd Faisal

    Aligarh Muslim University, India

    1 publications · 51 citations

    1

    h-index

  4. TY
    Tao Yang

    Northeastern University, China

    1 publications · 20 citations

    1

    h-index

  5. DK
    Dhruba K. Bhattacharyya

    Tezpur University, India

    1 publications · 246 citations

    1

    h-index

1–5 of 97

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 102 citations

    3

    h-index

  2. Intelligent Data Analysis

    IDA · 1 publications · 11 citations

    1

    h-index

  3. Journal of Environmental Informatics

    1 publications · 15 citations

    1

    h-index

  4. Neurocomputing

    1 publications · 50 citations

    1

    h-index

  5. 1

    h-index

1–5