Learning TAN from incomplete data

Abstract

Tree augmented Naive Bayes (TAN) classifier is a good tradeoff between the model complexity and learnability in practice. Since there are few complete datasets in real world, in this paper, we develop research on how to efficiently learn TAN from incomplete data. We first present an efficient method that could estimate conditional Mutual Information directly from incomplete data. And then we extend basic TAN learning algorithm to incomplete data using our conditional Mutual Information estimation method. Finally, we carry out experiments to evaluate the extended TAN and compare it with basic TAN. The experimental results show that the accuracy of the extended TAN is much higher than that of basic TAN on most of the incomplete datasets. Despite more time consumption of the extended TAN compared with basic TAN, it is still acceptable. Our conditional Mutual Information estimation method can be easily combined with other techniques to improve TAN further. © Springer-Verlag Berlin Heidelberg 2005.