Outlier Handling

Outlier Handling is a data preprocessing technique in feature engineering that identifies and mitigates the influence of anomalous observations deviating significantly from the majority of data points to improve model robustness and accuracy.

3.3K

Publications

266.2K

Citations

8K

Authors

2.4K

Institutions

Publications per year

2017–2026

1.2K

Authors

8K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    21 publications · 1K citations

    17

    h-index

  2. PJ
    Peter J. Rousseeuw

    University of Antwerp, Belgium

    17 publications · 4.3K citations

    16

    h-index

  3. AC
    Anthony C. Atkinson

    London School of Economics and Political Science, United Kingdom

    15 publications · 2.8K citations

    15

    h-index

  4. MH
    Mia Hubert

    KU Leuven, Belgium

    14 publications · 2.1K citations

    14

    h-index

  5. JM
    Jiayi Ma

    Wuhan University, China

    13 publications · 2K citations

    13

    h-index

1–5 of 8K

Venues

  1. Journal of the American Statistical Association

    73 publications · 24.1K citations

    45

    h-index

  2. Technometrics

    72 publications · 8.2K citations

    39

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 43 publications · 9.3K citations

    36

    h-index

  4. arXiv (Cornell University)

    Repository · 88 publications · 4.3K citations

    29

    h-index

  5. IEEE Transactions on Signal Processing

    47 publications · 2.1K citations

    27

    h-index

1–5