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Fuzzy versus Nonfuzzy in combining classifiers designed by boosting
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DOI：10.1109/TFUZZ.2003.819842.png)
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Abstract
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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 designed by Boosting. We ran two-fold cross-validation experiments on six benchmark data sets to compare the fuzzy and nonfuzzy combination methods. On the fuzzy side we used the fuzzy integral and the decision templates with different similarity measures. On the nonfuzzy side we tried the weighted majority vote as well as simple combiners such as the majority vote, minimum, maximum, average, product, and the Naive-Bayes combination. In our experiments, the fuzzy combination methods performed consistently better than the nonfuzzy methods. The weighted majority vote showed a stable performance, though slightly inferior to the performance of the fuzzy combiners.
Keywords:
Adaboost
classifier combination
decision templates
ensembles of classifiers created by Boosting
fuzzy integral
weighted majority vote
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