Boosted Bayesian Network Classifiers

Abstract

The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning. In this paper we present Boosted Bayesian Network Classifiers, a framework to combine discriminative data-weighting with generative training of intermediate models. We show that Boosted Bayesian network Classifiers encompass the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal. This framework can be easily extended to temporal Bayesian network models including HMM and DBN. On a large suite of benchmark data-sets, this approach outperforms generative graphical models such as naive Bayes, TAN, unrestricted Bayesian network and DBN in classification accuracy. Boosted Bayesian network classifiers have comparable or better performance in comparison to other discriminatively trained graphical models including ELR-NB, ELR-TAN, BNC-2P, BNC-MDL and CRF. Furthermore, boosted Bayesian networks require significantly less training time than all of the competing methods.