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Dimensionality reduction is useful for improving the performance of Bayesian networks. In this paper we suggest an effective method of modeling categorical and numerical variables of the mixed data with different Bayesian classifiers. Such an approach reduces output sensitivity to input changes by applying feature extraction and selection, and empirical studies on UCI benchmarking data show that our approach has clear advantages with respect to the classification accuracy.

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