Research On Learning Methods And Application Of Discriminative Bayesian Networks

Bayesian network classifier has been widely applied in many domains. Two types of Bayesian network classifiers have been extended so that they can handle more complex problem. One type of Bayesian network classifier is extended in structure and the representational classifier is such as na?ve bayes and TAN. Other type of extended Bayesian network classifier is discriminative Bayesian networks classifier. Bayesian networks have two learning paradigms such as generative learning and discriminative learning. Generative learning of Bayesian networks have been widely researched but discriminative learning is fewly researched. In this paper discriminative Bayesian networks learning from unbalanced data, attribute missing data and label missing data are researched. The main content and fruits of this paper are outlined as follows:In this dissertation, generative and discriminative learning methods of Bayesian networks are reviewed and then they are compared from various points of view.To study discriminative Bayesian networks learning from unbalanced data, cost sensitive learning method of discriminative Bayesian networks is presented. Cost sensitive Bayesian networks take into account classification cost. In the cost sensitive parameter learning, a cost sensitive loss function is proposed and in the cost sensitive structure learning a cost sensitive criterion is used in model selection.To study discriminative Bayesian networks learning from attribute missing data, CEM learning method is given. A Q function that has monotonic and convergence log conditional likelihood is proposed. However, convergency CEM has some faults when it is used in discriminative Bayesian networks classifier learning. Accordingly a simple Q function is proposed to replace it. Then, in M step of CEM optimal procedure is replaced by a search procedure of gradient descent. The approximation E step and M step make CEM simpler and more effective than standard CEM.To study discriminative Bayesian networks learning from label missing data, semi-supervised learning and active learning methods are presented. Fistly a generative-discriminative hybrid method is studied. In hybrid method, objective function is weighted between log joint likelihood of unlabeled data and log conditional likelihood of labeled data. Then active learning based on cost sensitive sampling method is proposed. In the method, two cost reduction sampling methods are proposed.In the end of this dissertation, tobacco quality is evaluated by discriminative Bayesian networks. This can be taken as auxiliary means in tobacco design.