Anomaly Classification

Anomaly Classification is a methodological framework within incident classification that identifies and categorizes deviations from normal behavior or expected patterns by distinguishing anomalous events from baseline or nominal conditions.

299

Publications

14.8K

Citations

1.1K

Authors

440

Institutions

Publications per year

2017–2026

195

Authors

1.1K

  1. GP
    Guansong Pang

    Singapore Management University, Singapore

    6 publications · 777 citations

    6

    h-index

  2. YY
    Yasser Yasami

    Shahid Beheshti University, Iran

    3 publications · 114 citations

    3

    h-index

  3. DP
    Dan Pei

    Tsinghua University, China

    3 publications · 324 citations

    3

    h-index

  4. NK
    Neeraj Kumar

    Thapar Institute of Engineering & Technology, India

    2 publications · 63 citations

    2

    h-index

  5. SG
    Shaogang Gong

    Queen Mary University of London, United Kingdom

    2 publications · 150 citations

    2

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 413 citations

    11

    h-index

  2. IEEE Access

    9 publications · 390 citations

    9

    h-index

  3. Multimedia Tools and Applications

    4 publications · 262 citations

    4

    h-index

  4. 3

    h-index

  5. Pattern Recognition

    3 publications · 308 citations

    3

    h-index

1–5