Concept Drift Modeling

Concept Drift Modeling is a computational framework within knowledge modeling that formally represents and detects temporal changes in the underlying data distribution or concept relationships over time, distinguishing it from static modeling approaches by its focus on dynamic concept evolution.

420

Publications

31.9K

Citations

1.2K

Authors

490

Institutions

Publications per year

2017–2026

249

Authors

1.2K

  1. JL
    Jie Lü

    University of Technology Sydney, Australia

    15 publications · 913 citations

    14

    h-index

  2. GZ
    Guangquan Zhang

    University of Technology Sydney, Australia

    15 publications · 913 citations

    14

    h-index

  3. AB
    Albert Bifet

    University of Waikato, New Zealand

    11 publications · 2.9K citations

    11

    h-index

  4. IŽ
    Indrė Žliobaitė

    University of Helsinki, Finland

    12 publications · 1.2K citations

    11

    h-index

  5. MP
    Mykola Pechenizkiy

    Eindhoven University of Technology, Netherlands

    10 publications · 672 citations

    10

    h-index

1–5 of 1.2K

Venues

  1. Expert Systems with Applications

    20 publications · 1.4K citations

    18

    h-index

  2. Neurocomputing

    15 publications · 1.9K citations

    15

    h-index

  3. IEEE Access

    15 publications · 757 citations

    14

    h-index

  4. arXiv (Cornell University)

    Repository · 12 publications · 783 citations

    11

    h-index

  5. Information Sciences

    12 publications · 836 citations

    11

    h-index

1–5