Combining Naive Bayes and Decision Tables

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This is an article published in Proceedings of Twenty-First International Florida Artificial Intelligence Research Society Conference, AAAI Press, Coconut Grove, Florida, USA, 15-17 May, 2008.

Abstract

We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction of decision tables. Naive Bayes and decision tables can both be trained efficientyly, and the same holds true for the combined semi-naive model. We show that the resulting ranker, compared to either component technique, frequently significantly increases AUC. For some datasets it significantly improves on both techniques. This is also the case when attribute selection is performed in naive Bayes and its semi-naive variant.

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Hall, M. & Frank, E.(2008). Combining Naive Bayes and Decision Tables. In D.L. Wilson & H. Chad (Eds), Proceedings of Twenty-First International Florida Artificial Intelligence Research Society Conference, AAAI Press, Coconut Grove, Florida, USA, 15-17 May, 2008(pp. 318-319).

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