Supervised Anomaly Detection

Supervised Anomaly Detection is a supervised learning framework in which a model is trained on labeled data containing both normal and anomalous instances to distinguish between the two classes based on explicit class labels.

185

Publications

9.3K

Citations

671

Authors

303

Institutions

Publications per year

2017–2026

155

Authors

671

  1. CS
    Chunhua Shen

    The University of Adelaide, Australia

    4 publications · 290 citations

    4

    h-index

  2. GP
    Guansong Pang

    Singapore Management University, Singapore

    4 publications · 290 citations

    4

    h-index

  3. SR
    Stefania Russo

    ETH Zurich, Switzerland

    2 publications · 110 citations

    2

    h-index

  4. CW
    Chengjie Wang

    Tencent (China), China

    2 publications · 116 citations

    2

    h-index

  5. JK
    Jinoh Kim

    East Texas A&M University, United States

    2 publications · 50 citations

    2

    h-index

1–5 of 671

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 221 citations

    11

    h-index

  2. Sensors

    4 publications · 132 citations

    4

    h-index

  3. Neural Computing and Applications

    3 publications · 204 citations

    3

    h-index

  4. Multimedia Tools and Applications

    3 publications · 159 citations

    3

    h-index

  5. IEEE Access

    3 publications · 109 citations

    3

    h-index

1–5