Multi-boosting

Multi-Boosting is an ensemble learning method that extends boosting by combining multiple base learners through multi-dimensional optimization to enhance generalization performance while maintaining robustness against overfitting.

12

Publications

1K

Citations

45

Authors

39

Institutions

Publications per year

2017–2026

6

Authors

45

  1. MZ
    Mohammad Zoynul Abedin

    Swansea University, United Kingdom

    1 publications · 26 citations

    1

    h-index

  2. KW
    Koen W. De Bock

    Institut d'Economie Scientifique Et de Gestion, France

    1 publications · 75 citations

    1

    h-index

  3. YC
    Yinghao Chen

    Central South University, China

    1 publications · 26 citations

    1

    h-index

  4. SV
    Sara V. Good

    University of Winnipeg, Canada

    1 publications · 37 citations

    1

    h-index

  5. FM
    Farnoush Mohammadi

    University of Tehran, Iran

    1 publications · 58 citations

    1

    h-index

1–5 of 45

Venues

  1. Information Sciences

    1 publications · 75 citations

    1

    h-index

  2. Applied Soft Computing

    1 publications · 520 citations

    1

    h-index

  3. PLoS ONE

    1 publications · 44 citations

    1

    h-index

  4. Geocarto International

    1 publications · 58 citations

    1

    h-index

  5. Pattern Recognition Letters

    1 publications · 22 citations

    1

    h-index

1–5