Ensemble Generation

Ensemble Generation is a methodological framework in machine learning that systematically constructs a collection of diverse base learners and combines their predictions to improve overall model accuracy and robustness, distinguished from other modeling approaches by its explicit focus on leveraging multiple heterogeneous or homogeneous models through strategic aggregation mechanisms.

357

Publications

33.2K

Citations

1.1K

Authors

483

Institutions

Publications per year

2017–2026

152

Authors

1.1K

  1. RS
    Robert Sabourin

    École de Technologie Supérieure, Canada

    7 publications · 584 citations

    7

    h-index

  2. DW
    David W. Opitz

    University of Montana, United States

    6 publications · 3.9K citations

    6

    h-index

  3. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    5 publications · 170 citations

    6

    h-index

  4. LI
    Ludmila I. Kuncheva

    Bangor University, United Kingdom

    5 publications · 2.5K citations

    5

    h-index

  5. XY
    Xin Yao

    University of Birmingham, United Kingdom

    5 publications · 407 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Monthly Weather Review

    12 publications · 3.6K citations

    12

    h-index

  2. arXiv (Cornell University)

    Repository · 14 publications · 315 citations

    11

    h-index

  3. Pattern Recognition

    9 publications · 975 citations

    9

    h-index

  4. Information Sciences

    8 publications · 350 citations

    8

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 8 publications · 938 citations

    8

    h-index

1–5