Semi-supervised Ensemble Learning

Semi-Supervised Ensemble Learning is an ensemble learning paradigm that integrates both labeled and unlabeled data within a multi-model framework to improve predictive performance by leveraging the structural patterns inferred from the unlabeled data while maintaining the supervised learning objective on the labeled subset.

169

Publications

8.5K

Citations

657

Authors

248

Institutions

Publications per year

2017–2026

100

Authors

657

  1. ZY
    Zhiwen Yu

    South China University of Technology, China

    10 publications · 683 citations

    10

    h-index

  2. HW
    Hau−San Wong

    City University of Hong Kong, Hong Kong SAR China

    8 publications · 588 citations

    8

    h-index

  3. JY
    Jane You

    Hong Kong Polytechnic University, Hong Kong SAR China

    8 publications · 603 citations

    8

    h-index

  4. IE
    Ioannis E. Livieris

    University of Patras, Greece

    8 publications · 374 citations

    8

    h-index

  5. GH
    Guoqiang Han

    South China University of Technology, China

    8 publications · 610 citations

    8

    h-index

1–5 of 657

Venues

  1. Neurocomputing

    6 publications · 248 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 463 citations

    5

    h-index

  3. Knowledge-Based Systems

    5 publications · 284 citations

    5

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 395 citations

    5

    h-index

  5. 4

    h-index

1–5