Classifier Fusion

Classifier Fusion is a decision-level integration strategy in multimodal machine learning that combines the outputs of multiple individual classifiers to improve overall predictive performance by leveraging complementary information and reducing classifier-specific biases.

4.1K

Publications

263.3K

Citations

13.1K

Authors

3.1K

Institutions

Publications per year

2017–2026

1.8K

Authors

13.1K

  1. JK
    Josef Kittler

    University of Surrey, United Kingdom

    26 publications · 7.2K citations

    20

    h-index

  2. FR
    Fabio Roli

    University of Cagliari, Italy

    17 publications · 1.6K citations

    16

    h-index

  3. RS
    Robert Sabourin

    École de Technologie Supérieure, Canada

    24 publications · 759 citations

    15

    h-index

  4. LI
    Ludmila I. Kuncheva

    Bangor University, United Kingdom

    14 publications · 3.6K citations

    14

    h-index

  5. JA
    Jón Atli Benediktsson

    University of Iceland, Iceland

    13 publications · 3.3K citations

    13

    h-index

1–5 of 13.1K

Venues

  1. IEEE Access

    101 publications · 4.6K citations

    41

    h-index

  2. Information Fusion

    59 publications · 6.5K citations

    36

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 45 publications · 14.6K citations

    35

    h-index

  4. Expert Systems with Applications

    48 publications · 4K citations

    32

    h-index

  5. Pattern Recognition Letters

    47 publications · 5K citations

    31

    h-index

1–5