Online Ensemble Learning

Online Ensemble Learning is an ensemble learning paradigm that incrementally updates a dynamic set of base learners in real-time as new data instances arrive sequentially, distinguishing itself from batch ensemble methods by its ability to adapt to non-stationary data distributions with limited computational resources and memory.

205

Publications

17K

Citations

630

Authors

292

Institutions

Publications per year

2017–2026

122

Authors

630

  1. XY
    Xin Yao

    University of Birmingham, United Kingdom

    7 publications · 1.5K citations

    7

    h-index

  2. LL
    Leandro L. Minku

    University of Birmingham, United Kingdom

    7 publications · 1.6K citations

    7

    h-index

  3. HB
    Hamed Bonab

    University of Massachusetts Amherst, United States

    5 publications · 259 citations

    5

    h-index

  4. RS
    Robert Sabourin

    École de Technologie Supérieure, Canada

    4 publications · 582 citations

    4

    h-index

  5. GD
    George D. C. Cavalcanti

    Universidade Federal de Pernambuco, Brazil

    4 publications · 582 citations

    4

    h-index

1–5 of 630

Venues

  1. Neurocomputing

    8 publications · 798 citations

    8

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 1.7K citations

    7

    h-index

  3. IEEE Access

    7 publications · 478 citations

    7

    h-index

  4. 6

    h-index

  5. arXiv (Cornell University)

    Repository · 6 publications · 181 citations

    6

    h-index

1–5