Ensemble Learning

Ensemble Learning is a machine learning paradigm that combines multiple base models within a statistical learning framework to improve predictive accuracy and robustness beyond what individual models can achieve.

18.9K

Publications

1.5M

Citations

53.8K

Authors

8.1K

Institutions

Publications per year

2017–2026

13.1K

Authors

53.8K

  1. PN
    Ponnuthurai Nagaratnam Suganthan

    Nanyang Technological University, Singapore

    50 publications · 5.6K citations

    35

    h-index

  2. XY
    Xin Yao

    University of Birmingham, United Kingdom

    53 publications · 8K citations

    35

    h-index

  3. FH
    Francisco Herrera

    Universidad de Granada, Spain

    38 publications · 14.2K citations

    34

    h-index

  4. BT
    Binh Thai Pham

    University Of Transport Technology, Vietnam

    40 publications · 5.3K citations

    34

    h-index

  5. RS
    Robert Sabourin

    École de Technologie Supérieure, Canada

    60 publications · 3.8K citations

    32

    h-index

1–5 of 53.8K

Venues

  1. Expert Systems with Applications

    352 publications · 31.8K citations

    87

    h-index

  2. IEEE Access

    494 publications · 32.3K citations

    82

    h-index

  3. arXiv (Cornell University)

    Repository · 512 publications · 55.7K citations

    77

    h-index

  4. Neurocomputing

    328 publications · 33.1K citations

    76

    h-index

  5. Pattern Recognition

    208 publications · 21.5K citations

    73

    h-index

1–5