Multiple Classifier Systems

Multiple Classifier Systems is a computational framework in ensemble learning that integrates the predictions of multiple distinct classifiers through a strategic combination mechanism to improve overall classification accuracy and robustness beyond what any individual classifier can achieve.

1.3K

Publications

111.6K

Citations

3.5K

Authors

1.3K

Institutions

Publications per year

2017–2026

392

Authors

3.5K

  1. JK
    Josef Kittler

    University of Surrey, United Kingdom

    18 publications · 6.8K citations

    16

    h-index

  2. FR
    Fabio Roli

    University of Cagliari, Italy

    16 publications · 1.7K citations

    15

    h-index

  3. LI
    Ludmila I. Kuncheva

    Bangor University, United Kingdom

    12 publications · 3.2K citations

    12

    h-index

  4. RS
    Robert Sabourin

    École de Technologie Supérieure, Canada

    13 publications · 671 citations

    11

    h-index

  5. GG
    Giorgio Giacinto

    University of Cagliari, Italy

    10 publications · 1.2K citations

    10

    h-index

1–5 of 3.5K

Venues

  1. Pattern Recognition

    27 publications · 2.9K citations

    22

    h-index

  2. Pattern Recognition Letters

    25 publications · 2K citations

    20

    h-index

  3. Applied Soft Computing

    19 publications · 1.1K citations

    16

    h-index

  4. Information Fusion

    16 publications · 2.4K citations

    15

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 15 publications · 10.9K citations

    14

    h-index

1–5