On pairwise naive bayes classifiers

Abstract

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 aggrega-tion 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. © Springer-Verlag Berlin Heidelberg 2007.