Discriminative vs. Generative Learning of Bayesian Network Classifiers

Authors:
Guzmán Santafé;Jose A. Lozano;Pedro Larrañaga
Affiliations:
Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Spain;Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Spain;Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Spain
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

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Abstract

Discriminative learning of Bayesian network classifiers has recently received considerable attention from the machine learning community. This interest has yielded several publications where new methods for the discriminative learning of both structure and parameters have been proposed. In this paper we present an empirical study used to illustrate how discriminative learning performs with respect to generative learning using simple Bayesian network classifiers such as naive Bayes or TAN, and we discuss when and why a discriminative learning is preferred. We also analyzed how log-likelihood and conditional log-likelihood scores guide the learning process of Bayesian network classifiers.