In this paper, we introduce a new method to improve the performance of combining boosting and naieve Bayesian. Instead of combining boosting and Naieve Bayesian learning directly, which was proved to be unsatisfactory to improve performance, we select the training samples dynamically by bootstrap method for the construction of naive Bayesian classifiers, and hence generate very different or unstable base classifiers for boosting. Besides, we devise a modification for the weight adjusting of boosting algorithm in order to achieve this goal: minimizing the overlapping errors of its constituent classifiers. We conducted series of experiments, which show that the new method not only has performance much better than naieve Bayesian classifiers or directly boosted naieve Bayesian ones, but also much quicker to obtain optimal performance than boosting stumps and boosting decision trees incorporated with naieve Bayesian learning.
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