Cascade Generalization

Cascade Generalization is a supervised ensemble learning method that sequentially combines multiple base learners, where each subsequent learner is trained on the original input features augmented with the predictions of all preceding learners, thereby enabling higher-level models to generalize from both data and prior model outputs.

28

Publications

1.3K

Citations

75

Authors

42

Institutions

Publications per year

2017–2026

9

Authors

75

  1. HZ
    Huimin Zhao

    University of Wisconsin–Milwaukee, United States

    3 publications · 87 citations

    3

    h-index

  2. SH
    Shiyi Han

    Apple (United Kingdom), United Kingdom

    2 publications · 91 citations

    2

    h-index

  3. SR
    Sudha Ram

    University of Arizona, United States

    2 publications · 67 citations

    2

    h-index

  4. TV
    Tran Van Phong

    Vietnam Academy of Science and Technology, Vietnam

    2 publications · 70 citations

    2

    h-index

  5. HV
    Hiep Van Le

    University Of Transport Technology, Vietnam

    2 publications · 70 citations

    2

    h-index

1–5 of 75

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 121 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 100 citations

    2

    h-index

  3. Geocarto International

    2 publications · 70 citations

    2

    h-index

  4. Neural Computation

    1 publications · 30 citations

    1

    h-index

  5. 1

    h-index

1–5