Concept Drift

Concept Drift is a phenomenon in information filtering whereby the statistical properties of the target variable or underlying data distribution change over time in an unforeseen manner, thereby compromising the performance of learning models trained on historical data.

904

Publications

59.3K

Citations

2.2K

Authors

785

Institutions

Publications per year

2017–2026

544

Authors

2.2K

  1. JL
    Jie Lü

    University of Technology Sydney, Australia

    31 publications · 1.8K citations

    25

    h-index

  2. GZ
    Guangquan Zhang

    University of Technology Sydney, Australia

    32 publications · 1.9K citations

    25

    h-index

  3. AB
    Albert Bifet

    University of Waikato, New Zealand

    19 publications · 3.3K citations

    17

    h-index

  4. IŽ
    Indrė Žliobaitė

    University of Helsinki, Finland

    15 publications · 1.4K citations

    14

    h-index

  5. LK
    Latifur Khan

    The University of Texas at Dallas, United States

    15 publications · 1.3K citations

    13

    h-index

1–5 of 2.2K

Venues

  1. 24

    h-index

  2. Expert Systems with Applications

    26 publications · 1.9K citations

    23

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 25 publications · 3.3K citations

    23

    h-index

  4. IEEE Access

    27 publications · 1.3K citations

    18

    h-index

  5. Neurocomputing

    16 publications · 1.9K citations

    16

    h-index

1–5