Fusion Rule Selection

Fusion Rule Selection is the systematic process of identifying and applying an optimal decision-level fusion strategy that combines heterogeneous information sources by evaluating their individual reliability, redundancy, and complementary characteristics to enhance overall classification or inference accuracy.

77

Publications

4.1K

Citations

198

Authors

112

Institutions

Publications per year

2017–2026

18

Authors

198

  1. FS
    Florentín Smarandache

    University of New Mexico, United States

    3 publications · 477 citations

    3

    h-index

  2. EP
    Edward Peh

    Institute for Infocomm Research, Singapore

    2 publications · 524 citations

    2

    h-index

  3. FR
    Fabio Roli

    University of Cagliari, Italy

    2 publications · 96 citations

    2

    h-index

  4. SA
    Saud Althunibat

    Al-Hussein Bin Talal University, Jordan

    2 publications · 68 citations

    2

    h-index

  5. SK
    Sébastien Konieczny

    Centre National de la Recherche Scientifique, France

    2 publications · 80 citations

    2

    h-index

1–5 of 198

Venues

  1. Information Fusion

    9 publications · 892 citations

    9

    h-index

  2. Information Sciences

    3 publications · 53 citations

    3

    h-index

  3. IEEE Transactions on Vehicular Technology

    3 publications · 533 citations

    3

    h-index

  4. Soft Computing

    3 publications · 93 citations

    3

    h-index

  5. Pattern Recognition Letters

    3 publications · 206 citations

    3

    h-index

1–5