Information-theoretic Fusion

Information-Theoretic Fusion is a decision-level fusion method that integrates multiple information sources by leveraging information-theoretic measures, such as entropy, mutual information, or divergence metrics, to optimize the combination process based on the quantification of uncertainty and statistical dependence among modalities.

545

Publications

30.2K

Citations

1.5K

Authors

632

Institutions

Publications per year

2017–2026

174

Authors

1.5K

  1. YD
    Yong Deng

    University of Electronic Science and Technology of China, China

    12 publications · 743 citations

    12

    h-index

  2. FS
    Florentín Smarandache

    University of New Mexico, United States

    9 publications · 813 citations

    9

    h-index

  3. EB
    Erik Blasch

    United States Air Force Research Laboratory, United States

    8 publications · 227 citations

    8

    h-index

  4. LC
    Luigi Chisci

    University of Florence, Italy

    7 publications · 376 citations

    7

    h-index

  5. ZX
    Zeshui Xu

    Sichuan University, China

    7 publications · 2.9K citations

    7

    h-index

1–5 of 1.5K

Venues

  1. Information Fusion

    44 publications · 2.7K citations

    30

    h-index

  2. Information Sciences

    13 publications · 697 citations

    13

    h-index

  3. International Journal of Intelligent Systems

    11 publications · 789 citations

    11

    h-index

  4. 11

    h-index

  5. Expert Systems with Applications

    8 publications · 212 citations

    8

    h-index

1–5