Regularization and Averaging of the Selective Naive Bayes classifier

Abstract

Naïve Bayes classifier has proved to be very effective on many real data applications. Its performances usually benefit from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a new regularization… (More)
DOI: 10.1109/IJCNN.2006.246637

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