Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction

Abstract

We introduce Ensembled Continuous Bayesian Networks (ECBN), an ensemble approach to learning salient dependence relationships and to predicting values for continuous data. By training individual Bayesian networks on both a subset of the data (bagging) and a subset of the attributes in the data (randomization), ECBN produces models for continuous domains that can be used to identify important variables in a dataset and to identify relationships between those variables. We use linear Gaussian distributions within our ensembles, providing efficient network-level inference. By ensembling these networks, we are able to represent nonlinear relationships. We empirically demonstrate that ECBN outperforms the meteorological forecast on a rainfall prediction task across the United States, and performs comparably to results reported for Random Forests.