Anomaly Score Normalization

Anomaly Score Normalization is a methodological procedure in anomaly detection that transforms raw anomaly scores into a standardized range to enable consistent comparison across different models or datasets by mitigating score scale heterogeneity.

10

Publications

495

Citations

35

Authors

15

Institutions

Publications per year

2017–2026

8

Authors

35

  1. JC
    Jian Cao

    Shanghai Jiao Tong University, China

    1 publications · 23 citations

    1

    h-index

  2. ZW
    Zhiqiang Wang

    East China University of Science and Technology, China

    1 publications · 16 citations

    1

    h-index

  3. JV
    John V. Monaco

    Pace University, United States

    1 publications · 21 citations

    1

    h-index

  4. YW
    Yushuang Wu

    Chinese University of Hong Kong, Shenzhen, China

    1 publications · 215 citations

    1

    h-index

  5. RZ
    Rong Zhang

    Tsinghua University, China

    1 publications · 34 citations

    1

    h-index

1–5 of 35

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 236 citations

    2

    h-index

  2. Neurocomputing

    1 publications · 16 citations

    1

    h-index

  3. Journal of Intelligent Manufacturing

    1 publications · 29 citations

    1

    h-index

  4. 1

    h-index

  5. Pattern Recognition

    2 publications · 66 citations

    1

    h-index

1–5