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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Naïve Bayes Ensembles with a Random Oracle
verfasst von : Juan J. Rodríguez, Ludmila I. Kuncheva
Erschienen in: Multiple Classifier Systems
Verlag: Springer Berlin Heidelberg
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Ensemble methods with Random Oracles have been proposed recently (Kuncheva and Rodríguez, 2007). A random-oracle classifier consists of a pair of classifiers and a fixed, randomly created oracle that selects between them. Ensembles of random-oracle decision trees were shown to fare better than standard ensembles. In that study, the oracle for a given tree was a random hyperplane at the root of the tree. The present work considers two random oracles types (linear and spherical) in ensembles of Naive Bayes Classifiers (NB). Our experiments show that ensembles based solely upon the spherical oracle (and no other ensemble heuristic) outrank Bagging, Wagging, Random Subspaces, AdaBoost.M1, MultiBoost and Decorate. Moreover,
all
these ensemble methods are better with any of the two random oracles than their standard versions without the oracles.