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.The objective of this research is to investigate the discriminative learning of BNs for various data mining tasks: classification, ranking, and probability estimation. In this research, we will investigate the following issues. (1) Effective discriminative BN learning: We are going to study discriminative structure learning that can effectively discover the underlying structure, and discriminative parameter learning that can compute the optimal parameters efficiently. (2) Scalable discriminative BN learning: One objective of this research is to develop efficient discriminative BN learning algorithms scalable to large-scale data mining. We are going to study various scaling up methods for  both structure learning and parameter learning. (3) Discriminative BN learning for ranking and probability estimation: In some data mining applications, accurate rankings and probability estimates are often desired. Is discriminative learning superior in terms of yielding accurate rankings and probabilities? How can BNs be discriminatively trained for this purpose? What kind of scoring functions and search strategies should be used? Our research will answer these questions.Due to the inherent features of discriminative learning, we would expect that this research could result in more effective and efficient data mining algorithms.