Heterogeneous Ensemble Modeling

Heterogeneous Ensemble Modeling is an ensemble learning approach that integrates multiple base learners of different types or algorithms to improve predictive performance by leveraging diverse model architectures and decision mechanisms.

84

Publications

4.1K

Citations

325

Authors

156

Institutions

Publications per year

2017–2026

66

Authors

325

  1. SC
    Sailesh Conjeti

    Technical University of Munich, Germany

    2 publications · 46 citations

    2

    h-index

  2. DW
    Dujuan Wang

    Sichuan University, China

    2 publications · 50 citations

    2

    h-index

  3. SC
    Somphop Chiaranai

    Ubon Ratchathani University, Thailand

    2 publications · 42 citations

    2

    h-index

  4. LW
    Lichao Wang

    UConn Health, United States

    2 publications · 46 citations

    2

    h-index

  5. RP
    Rapeepan Pitakaso

    Ubon Ratchathani University, Thailand

    2 publications · 42 citations

    2

    h-index

1–5 of 325

Venues

  1. F1000Research

    3 publications · 59 citations

    3

    h-index

  2. Procedia Computer Science

    3 publications · 135 citations

    3

    h-index

  3. Neurocomputing

    3 publications · 214 citations

    3

    h-index

  4. IEEE Access

    3 publications · 117 citations

    3

    h-index

  5. Applied Sciences

    2 publications · 75 citations

    2

    h-index

1–5