Generalized Additive Bayesian Network Classifiers
Jianguo Li, Changshui Zhang, Tao Wang, Yimin Zhang
Bayesian network classifiers (BNC) have received considerable attention in machine learning field. Some special structure BNCs have been proposed and demonstrate promise performance. However, recent works show that structure learning in BNs may lead to a non-negligible posterior problem, i.e, there might be many structures have similar posterior scores. In this paper, we propose a generalized additive Bayesian network classifiers, which transfers the structure learning problem to a generalized additive models (GAM) learning problem. We first generate a series of very simple BNs, and put them in the framework of GAM, then adopt a gradient-based algorithm to learn the combining parameters, and thus construct a more powerful classifier. On a large suite of benchmark data sets, the proposed approach outperforms many traditional BNCs, such as naive Bayes, TAN, etc, and achieves comparable or better performance in comparison to boosted Bayesian network classifiers.