Ensemble Methods

Ensemble Methods is a predictive modeling technique that combines the predictions of multiple base models to improve overall accuracy and robustness by reducing variance, bias, or enhancing generalization beyond the capabilities of individual models.

27.1K

Publications

2M

Citations

74.7K

Authors

9.9K

Institutions

Publications per year

2017–2026

17.6K

Authors

74.7K

  1. BT
    Binh Thai Pham

    University Of Transport Technology, Vietnam

    73 publications · 10.7K citations

    54

    h-index

  2. DT
    Dieu Tien Bui

    Duy Tan University, Vietnam

    46 publications · 8.2K citations

    42

    h-index

  3. PN
    Ponnuthurai Nagaratnam Suganthan

    Nanyang Technological University, Singapore

    51 publications · 6.4K citations

    36

    h-index

  4. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    67 publications · 8.4K citations

    35

    h-index

  5. XY
    Xin Yao

    University of Birmingham, United Kingdom

    55 publications · 7.9K citations

    35

    h-index

1–5 of 74.7K

Venues

  1. Expert Systems with Applications

    437 publications · 38K citations

    98

    h-index

  2. Monthly Weather Review

    365 publications · 46.7K citations

    97

    h-index

  3. IEEE Access

    637 publications · 38.6K citations

    91

    h-index

  4. Pattern Recognition

    242 publications · 25.8K citations

    80

    h-index

  5. arXiv (Cornell University)

    Repository · 578 publications · 55.2K citations

    78

    h-index

1–5