Homogeneous Ensemble Learning

Homogeneous Ensemble Learning is an ensemble learning approach in which multiple instances of the same type of base learner are combined to improve predictive performance, differing from heterogeneous ensembles by employing a uniform class of models across the ensemble.

33

Publications

2K

Citations

126

Authors

59

Institutions

Publications per year

2017–2026

24

Authors

126

  1. RK
    R. K. Agrawal

    Jawaharlal Nehru University, India

    2 publications · 27 citations

    2

    h-index

  2. RA
    Ratnadip Adhikari

    Jawaharlal Nehru University, India

    2 publications · 27 citations

    2

    h-index

  3. VK
    Vinoth Kumar Venkatesan

    Vellore Institute of Technology University, India

    1 publications · 85 citations

    1

    h-index

  4. AB
    Arief Boediono

    Yamaguchi University, Japan

    1 publications · 10 citations

    1

    h-index

  5. YA
    Yasar Ayaz

    National University of Sciences and Technology, Pakistan

    1 publications · 34 citations

    1

    h-index

1–5 of 126

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 59 citations

    2

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  2. Geoscience Frontiers

    1 publications · 154 citations

    1

    h-index

  3. Machine Vision and Applications

    1 publications · 17 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5