Label Error Detection

Label Error Detection is a methodological construct in machine learning that identifies incorrect annotations within labeled datasets by distinguishing anomalous label assignments from ground-truth-consistent labels based on model predictions, data patterns, or auxiliary validation criteria.

45

Publications

1.7K

Citations

199

Authors

79

Institutions

Publications per year

2017–2026

27

Authors

199

  1. DG
    Donghai Guan

    Nanjing University of Aeronautics and Astronautics, China

    3 publications · 93 citations

    3

    h-index

  2. WY
    Weiwei Yuan

    Nanjing University of Aeronautics and Astronautics, China

    3 publications · 93 citations

    3

    h-index

  3. UR
    Umaa Rebbapragada

    Jet Propulsion Laboratory, United States

    2 publications · 28 citations

    2

    h-index

  4. CG
    Curtis G. Northcutt

    Massachusetts Institute of Technology, United States

    2 publications · 164 citations

    2

    h-index

  5. LW
    Liqiang Wang

    University of Central Florida, United States

    2 publications · 108 citations

    2

    h-index

1–5 of 199

Venues

  1. IEEE Access

    3 publications · 149 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 113 citations

    2

    h-index

  3. Bioinformatics

    2 publications · 107 citations

    2

    h-index

  4. Knowledge-Based Systems

    2 publications · 42 citations

    2

    h-index

  5. npj Computational Materials

    1 publications · 12 citations

    1

    h-index

1–5