Combining decision tree and Naive Bayes for classification

Authors:
Li-Min Wang;Xiao-Lin Li;Chun-Hong Cao;Sen-Miao Yuan
Affiliations:
College of Computer Science and Technology, JiLin University, ChangChun 130012, People's Republic of China;National Laboratory for Novel Software Technology, NanJing University, NanJing 210093, People's Republic of China;Department of Computer Science, NorthEast University, ShenYang 230012, People's Republic of China;College of Computer Science and Technology, JiLin University, ChangChun 130012, People's Republic of China
Venue:
Knowledge-Based Systems
Year:
2006

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Abstract

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.