A Framework for Incorporating Class Priors into Discriminative Classification

Abstract. Discriminative and generative methods provide two distinct approaches to machine learning classification. One advantage of generative approaches is that they naturally model the prior class distributions. In contrast, discriminative approaches directly model the conditional distribution of class given inputs, so the class priors are only implicitly obtained if the input density is known. In this paper, we propose a framework for incorporating class prior proportions into discriminative methods in order to improve their classification accuracy. The basic idea is to enforce that the distribution of class labels predicted on the test data by the discriminative model is consistent with the class priors. Therefore, the discriminative model has to not only fit the training data well but also predict class labels for the test data that are consistent with the class priors. Experiments on five different UCI datasets and one image database show that this framework is effective in impr...

• Type: Conference
• Year: 2005
• Where: PAKDD
• Authors: Rong Jin, Yi Liu
• Keywords: Class Priors, Data Mining, Discriminative Model, PAKDD 2005, Test Data