One-versus-one Classification

One-Versus-One Classification is a multi-class classification strategy in which a binary classifier is trained for every pair of classes, distinguishing one class from another, and predictions are made by majority voting among all classifiers.

66

Publications

6.7K

Citations

231

Authors

113

Institutions

Publications per year

2017–2026

21

Variants

One-against-one ClassificationOvo Classification

Authors

231

  1. FH
    Francisco Herrera

    Universidad de Granada, Spain

    4 publications · 1K citations

    4

    h-index

  2. MG
    Mikel Galar

    Universidad Pública de Navarra (UPNA), Spain

    2 publications · 858 citations

    2

    h-index

  3. BK
    Bartosz Krawczyk

    Wrocław University of Science and Technology, Poland

    2 publications · 146 citations

    2

    h-index

  4. ES
    Erik Scheme

    University of New Brunswick, Canada

    2 publications · 266 citations

    2

    h-index

  5. EB
    Edurne Barrenechea

    Universidad Pública de Navarra (UPNA), Spain

    2 publications · 858 citations

    2

    h-index

1–5 of 231

Venues

  1. Knowledge-Based Systems

    4 publications · 286 citations

    4

    h-index

  2. Pattern Recognition

    3 publications · 935 citations

    3

    h-index

  3. 2

    h-index

  4. IEEE Transactions on Medical Imaging

    1 publications · 26 citations

    1

    h-index

  5. Journal of Machine Learning Research

    JMLR · 1 publications · 897 citations

    1

    h-index

1–5