Class Imbalance

Class Imbalance is a data distribution phenomenon in machine learning whereby the number of instances in one class significantly outweighs those in other classes, leading to potential biases in model training and performance, particularly in object detection and risk prediction tasks.

4.2K

Publications

345.6K

Citations

11.4K

Authors

2.8K

Institutions

Publications per year

2017–2026

3.3K

Authors

11.4K

  1. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    82 publications · 11.4K citations

    41

    h-index

  2. FH
    Francisco Herrera

    Universidad de Granada, Spain

    41 publications · 9.3K citations

    34

    h-index

  3. BK
    Bartosz Krawczyk

    Wrocław University of Science and Technology, Poland

    24 publications · 5.2K citations

    21

    h-index

  4. XY
    Xin Yao

    University of Birmingham, United Kingdom

    23 publications · 5K citations

    20

    h-index

  5. JV
    Jason Van Hulse

    Florida Atlantic University, United States

    22 publications · 4.9K citations

    20

    h-index

1–5 of 11.4K

Venues

  1. IEEE Access

    181 publications · 12.8K citations

    63

    h-index

  2. Information Sciences

    73 publications · 11.8K citations

    47

    h-index

  3. Knowledge-Based Systems

    71 publications · 5.8K citations

    42

    h-index

  4. Expert Systems with Applications

    83 publications · 9.6K citations

    42

    h-index

  5. Neurocomputing

    71 publications · 5.8K citations

    41

    h-index

1–5