Semi-supervised Anomaly Detection

Semi-Supervised Anomaly Detection is a methodological framework within anomaly detection that leverages a subset of labeled data—typically only normal instances—combined with unlabeled data to learn a model that distinguishes anomalous from normal behavior by assuming most training data are normal while identifying deviations in time series observations.

295

Publications

16.2K

Citations

1K

Authors

418

Institutions

Publications per year

2017–2026

247

Authors

1K

  1. WM
    Wannes Meert

    KU Leuven, Belgium

    4 publications · 253 citations

    4

    h-index

  2. WX
    Weiying Xie

    Xidian University, China

    3 publications · 219 citations

    3

    h-index

  3. MM
    Milad Memarzadeh

    University of California, Berkeley, United States

    3 publications · 61 citations

    3

    h-index

  4. QD
    Qian Du

    Mississippi State University, United States

    3 publications · 219 citations

    3

    h-index

  5. YL
    Yunsong Li

    Xidian University, China

    3 publications · 219 citations

    3

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 446 citations

    14

    h-index

  2. IEEE Access

    13 publications · 784 citations

    12

    h-index

  3. Information Sciences

    7 publications · 980 citations

    7

    h-index

  4. 5

    h-index

  5. Expert Systems with Applications

    5 publications · 102 citations

    5

    h-index

1–5