Learning Bayesian Networks Using Feature Selection
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:450-456, 1995.
This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features which maximizes predictive accuracy prior to the network learning phase. We examine explicitly the effects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks which are computationally simpler to evaluate and which display predictive accuracy comparable to that of Bayesian networks which model all attributes.