Su, Jiang and Zhang, Harry and Ling, Charles X. and Matwin, Stan
Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameter learning can take two different approaches: generative and discriminative learning. While generative parameter learning is more efficient, discriminative parameter learning is more effective. In this paper, we propose a simple, efficient, and effective discriminative parameter learning method, called \emph{Discriminative Frequency Estimate} (DFE), which learns parameters by discriminatively computing frequencies from data. Empirical studies show that the DFE algorithm integrates the advantages of both generative and discriminative learning: it performs as well as the state-of-the-art discriminative parameter learning method ELR in accuracy, but is significantly more efficient.
Discussion