Most original classification algorithms pursue to minimize the error rate, while the differences between types
of misclassification errors and imbalanced dataset are ignored. Therefore, cost-sensitive probabilistic neural network (CS-
PNN) is proposed for the problem in this paper, in which cost-sensitive mechanism is introduced into probabilistic neural
network. CS-PNN is aimed to minimize expected cost, and the misclassification rate is replaced by expectation cost. Class
labels of new instances are predicted by using Bayes decision rules based on minimizing expected cost. The effectiveness
of the algorithm is verified by industrial data and dataset of German Credit. Experimental results show that CS-PNN is
characterized by high recognition for faults, strong ability of generalization and short modeling time.