Efficient Learning of Selective Bayesian Network Classifiers

Abstract

In this paper, we present a computationally efficient method for inducing selective Bayesian network classifiers. Our approach is to use informationtheoretic metrics to efficiently select a subset of attributes from which to learn the classifier. We explore three conditional, information-theoretic metrics that are extensions of metrics used extensively in decision tree learning, namely Quinlan's gain and gain ratio metrics and Mantaras's distance metric. We experimentally show that the algorithms based on gain ratio and distance metric learn selective Bayesian networks that have predictive accuracies as good as or better than those learned by existing selective Bayesian network induction approaches (K2-AS), but at a significantly lower computational cost. We prove that the subset-selection phase of these information-based algorithms has polynomial complexity as compared to the worst-case exponential time complexity of the corresponding phase in K2-AS. We also compare the performance o...