Chapter

Advances in Knowledge Discovery and Data Mining

Volume 3518 of the series Lecture Notes in Computer Science pp 568-577

A Framework for Incorporating Class Priors into Discriminative Classification

  • Rong JinAffiliated withDepartment of Computer Science and Engineering, Michigan State University
  • , Yi LiuAffiliated withDepartment of Computer Science and Engineering, Michigan State University

* Final gross prices may vary according to local VAT.

Get Access

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 improving the classification accuracy when the training data and the test data come from the same class proportions, even if the test data does not have exactly the same feature distribution as the training data.