Pairwise Optimization of Bayesian Classifiers for Multi-Class Cost-Sensitive Learning

In this paper, we present a new approach to enhance the performance of Bayesian classifiers. Our method relies on the combination of two ideas: pairwise classification on the one hand, and threshold optimization on the other hand. Introducing one threshold per pair of classes increases the expressivity of the model, therefore its performance on complex problems such as cost-sensitive problems increases as well. Indeed a comparison of our algorithm to other cost-sensitive approaches shows that it reduces the total misclassification cost.