Concept Drift Adaptation

Concept Drift Adaptation is a computational learning strategy that enables a model to dynamically adjust its parameters or structure in response to non-stationary data distributions, thereby maintaining predictive accuracy as underlying patterns evolve over time.

285

Publications

16.1K

Citations

830

Authors

374

Institutions

Publications per year

2017–2026

184

Authors

830

  1. GZ
    Guangquan Zhang

    University of Technology Sydney, Australia

    16 publications · 731 citations

    14

    h-index

  2. JL
    Jie Lü

    University of Technology Sydney, Australia

    16 publications · 731 citations

    14

    h-index

  3. AB
    Albert Bifet

    University of Waikato, New Zealand

    12 publications · 1.2K citations

    11

    h-index

  4. BP
    Bernhard Pfahringer

    University of Waikato, New Zealand

    10 publications · 1.2K citations

    10

    h-index

  5. YS
    Yiliao Song

    University of Technology Sydney, Australia

    9 publications · 351 citations

    9

    h-index

1–5 of 830

Venues

  1. 13

    h-index

  2. Expert Systems with Applications

    11 publications · 898 citations

    11

    h-index

  3. Neurocomputing

    10 publications · 520 citations

    10

    h-index

  4. arXiv (Cornell University)

    Repository · 9 publications · 650 citations

    9

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 8 publications · 378 citations

    8

    h-index

1–5