On Pairwise Naive Bayes Classifiers

Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can be applied to a Naive Bayes learner. The key result is that the pairwise variant of Naive Bayes is equivalent to a regular Naive Bayes. This result holds for several aggregation techniques for combining the predictions of the individual classifiers, including the commonly used voting and weighted voting techniques. On the other hand, Naive Bayes with one-against-all binarization is not equivalent to a regular Naive Bayes. Apart from the theoretical results themselves, the paper offers a discussion of their implications.

• Type: Conference
• Year: 2007
• Where: ECML
• Authors: Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier
• Keywords: Commonly Used Voting, ECML 2007, Machine Learning, Naive Bayes, Regular Naive Bayes