Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting

In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other state-of-the-art methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that it applies only to discrete attributes, and thus, continuous attributes must be prediscretized. In this paper, we extend TAN to deal with continuous attributes directly via parametric (e.g., Gaussians) and semiparametric (e.g., mixtureof Gaussians)conditionalprobabilities. The result is a classifier that can represent and combine both discrete and continuous attributes. In addition, we propose a new method that takes advantage of the modeling language of Bayesian networks in order to represent attributes both in discrete and continuous form simultaneously, and use both versions in the classifica...

• Type: Conference
• Year: 1998
• Where: ICML
• Authors: Moisés Goldszmidt, Nir Friedman, Thomas J. Lee
• Keywords: Continuous Attributes, Continuous Tan Models, ICML 1998, Machine Learning, TAN Classifier