"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting.

"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting.

Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers...

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Bibliographic Details
Published in:IEEE Transactions on fuzzy systems 11, 6 (2003).
Main Author: Kuncheva, L.I
Format: Article
Language:English
Subjects:
AdaBoost.
Boosting.
Naive-Bayes combination.
Classifier ensembles.
Fuzzy combination methods.
Fuzzy decision.
Fuzzy integral.
Nonfuzzy combination methods.
Similarity measures.
Two-fold cross-validation experiments.
Weighted majority vote.