Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers

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The authors introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. The authors propose two methods for establishing an order of N features. They are based on the conditional mutual information and classification rate (i.e., risk), respectively. They present results on 25 data sets from the UCI repository, for phonetic classification using the TIMIT database, for a visual surface inspection task, and for two handwritten digit recognition tasks. The authors provide classification performance for both discriminative and generative parameter learning on both discriminatively and generatively structured networks.