Random one-dependence estimators
Pattern Recognition Letters
Double-layer bayesian classifier ensembles based on frequent itemsets
International Journal of Automation and Computing
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Numerous approaches have been proposed to relax the conditional independence assumption of naive bayes, the accuracy performance was indeed improved relative to naive bayes when the assumption is violated. But most of the previous approaches treated the attribute relation in the same way for all class labels. In practice, this relation may be different for different class labels. This paper proposes a novel approach, by which the posterior probability of dif- ferent class label is evaluated using different attribute re- lation. Experiment results indicate that the new approach obtains comparative performance relative to other modern Bayesian classifiers on some datasets, and on some other datasets it outperforms the others.