Combining decision tree and Naive Bayes for classification

Decision tree is useful to obtain a proper set of rules from a large amount of instances. However, it has difficulty in obtaining the relationship between continuous-valued data points. We propose in this paper a novel algorithm, Self-adaptive NBTree, which induces a hybrid of decision tree and Naive Bayes. The Bayes measure, which is used to construct decision tree, can directly handle continuous attributes and automatically find the most appropriate boundaries for discretization and the number of intervals. The Naive Bayes node helps to solve overgeneralization and overspecialization problems which are often seen in decision tree. Experimental results on a variety of natural domains indicate that Self-adaptive NBTree has clear advantages with respect to the generalization ability.