Probability Fusion

Probability Fusion is a late fusion technique that combines probabilistic outputs from multiple models or modalities by aggregating their predicted probability distributions to produce a final decision, distinguished by its reliance on probabilistic calibration and combination strategies such as weighted averaging, product rules, or Bayesian integration.

27

Publications

1.1K

Citations

95

Authors

48

Institutions

Publications per year

2017–2026

11

Authors

95

  1. YC
    Ying Chen

    Guangdong University of Technology, China

    1 publications · 156 citations

    1

    h-index

  2. XZ
    Xin Zhou

    Wuhan University, China

    1 publications · 80 citations

    1

    h-index

  3. XJ
    Xuebo Jin

    Beijing Technology and Business University, China

    1 publications · 25 citations

    1

    h-index

  4. FS
    Florentín Smarandache

    University of New Mexico, United States

    1 publications · 123 citations

    1

    h-index

  5. FT
    Fergus Toolan

    University College Dublin, Ireland

    1 publications · 14 citations

    1

    h-index

1–5 of 95

Venues

  1. IEEE Access

    1 publications · 25 citations

    1

    h-index

  2. 1

    h-index

  3. IEEE Transactions on Fuzzy Systems

    TFS · 1 publications · 37 citations

    1

    h-index

  4. Information Sciences

    1 publications · 32 citations

    1

    h-index

  5. IEEE Geoscience and Remote Sensing Letters

    GRSL · 1 publications · 69 citations

    1

    h-index

1–5