Cost-Sensitive Parameters Estimation Method of Bayesian Networks

Misclassiflcation cost is usually unequal for different class. Parameters estimation methods of Bayesian networks such as generative method based on log joint likelihood loss and discriminative method based on log conditional likelihood loss suppose that misclassification cost of every class is equal. Accordingly, this paper develops a cost-sensitive parameters estimation method so that Bayesian networks can be used for cost sensitive classification. A cost sensitive loss function and an extended cost sensitive loss function are defined based on classification cost matrix and extended classification cost matrix respectively. The two cost-sensitive loss functions are both extension of log conditional likelihood loss in form. In experiment cost-sensitive Bayesian networks are compared with the corresponding generative Bayesian networks and discriminative Bayesian networks. Experiment results show that cost sensitive Bayesian networks with extended cost sensitive loss has preferable classification performance when used for cost sensitive classification.