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.
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2017–2026
392
Parents
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Statistical Comparisons of Classifiers over Multiple Data Sets
Janez Demšar · 2006 · 11.2K citations
Experiments with a new boosting algorithm
Yoav Freund, Robert E. Schapire · 1996 · 7.6K citations
Josef Kittler, M. Hatef, Robert P. W. Duin et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1998 · 5.3K citations
Ensemble based systems in decision making
Robi Polikar · IEEE Circuits and Systems Magazine · 2006 · 2.9K citations
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University of Surrey
Guildford, United Kingdom
21 h-index
45 publications
13.9K citations

University of Cagliari
Cagliari, Italy
17 h-index
45 publications
4.7K citations

Chinese Academy of Sciences
Beijing, China
16 h-index
45 publications
1.5K citations

Wrocław University of Science and Technology
Wroclaw, Poland
13 h-index
29 publications
1.8K citations

École de Technologie Supérieure
Montreal, Canada
12 h-index
33 publications
1.6K citations

Concordia University
Montreal, Canada
12 h-index
28 publications
9.1K citations
University of Kent
Canterbury, United Kingdom
11 h-index
23 publications
505 citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
23 publications
901 citations
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27 publications · 2.9K citations
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25 publications · 2K citations
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19 publications · 1.1K citations
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16 publications · 2.4K citations
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TPAMI · 15 publications · 10.9K citations
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