Missing Value Treatment

Missing Value Treatment is a data preprocessing technique that specifically addresses the absence of data points in a dataset by identifying, imputing, or removing missing values to ensure the integrity and usability of the data for analysis.

27

Publications

1.8K

Citations

71

Authors

38

Institutions

Publications per year

2017–2026

7

Authors

71

  1. LS
    Linda S. Chan

    University of Southern California, United States

    2 publications · 80 citations

    2

    h-index

  2. OJ
    Olive Jean Dunn

    University of California, Los Angeles, United States

    2 publications · 80 citations

    2

    h-index

  3. CR
    Chiang‐Shan R. Li

    Yale University, United States

    1 publications · 71 citations

    1

    h-index

  4. DB
    Debanjan Banerjee

    National Institute of Mental Health and Neurosciences, India

    1 publications · 54 citations

    1

    h-index

  5. RP
    Ryan P. McLynn

    Yale University, United States

    1 publications · 39 citations

    1

    h-index

1–5 of 71

Venues

  1. 2

    h-index

  2. International Journal of Intelligent Systems

    1 publications · 13 citations

    1

    h-index

  3. Information Sciences

    1 publications · 71 citations

    1

    h-index

  4. Atmospheric Research

    1 publications · 52 citations

    1

    h-index

  5. Biometrical Journal

    1 publications · 11 citations

    1

    h-index

1–5