A Survey of Multiple Classifier Systems as Hybrid Systems. Woźniak, M., Graña, M., & Corchado, E. 16:3–17. ![A Survey of Multiple Classifier Systems as Hybrid Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed.
@article{wozniakSurveyMultipleClassifier2014,
title = {A Survey of Multiple Classifier Systems as Hybrid Systems},
author = {Woźniak, Michał and Graña, Manuel and Corchado, Emilio},
date = {2014-03},
journaltitle = {Information Fusion},
volume = {16},
pages = {3--17},
issn = {1566-2535},
doi = {10.1016/j.inffus.2013.04.006},
url = {https://doi.org/10.1016/j.inffus.2013.04.006},
abstract = {A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13401831,classification,ensemble,hybridisation,machine-learning,modelling,multiplicity,review}
}
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