Discriminative learning of Bayesian networks for data mining applications

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Bayesian networks (BNs) have been widely used in data mining applications, in which learning a BN from data is crucial. Most BN learning algorithms learn a joint distribution over all the variables to best fit the training data, i.e. generative learning. Discriminative learning directly targets a task-specific model, instead of a generative model, and demonstrates superior performance. Discriminatively learning a BN includes two steps: structure learning and parameter learning. Structure learning determines the structure of a BN, and parameter learning sets the local distribution for each variable.