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Hierarchical mixtures of naive Bayes classifiers
Full text linkNaive Bayes classifiers tend to perform very well on a large number
of problem domains, although their representation power is quite limited
compared to more sophisticated machine learning algorithms. In this pa-
per we study combining multiple naive Bayes classifiers by using the hierar-
chical mixtures of experts system. This system, which we call hierarchical
mixtures of naive Bayes classifiers, is compared to a simple naive Bayes
classifier and to using bagging and boosting for combining multiple clas-
sifiers. Results on 19 data sets from the UCI repository indicate that the
hierarchical mixtures architecture in general outperforms the other meth-
ods
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