Discriminative vs. Generative Classifiers for Cost Sensitive Learning

  • Chris Drummond
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)


This paper experimentally compares the performance of discriminative and generative classifiers for cost sensitive learning. There is some evidence that learning a discriminative classifier is more effective for a traditional classification task. This paper explores the advantages, and disadvantages, of using a generative classifier when the misclassification costs, and class frequencies, are not fixed. The paper details experiments built around commonly used algorithms modified to be cost sensitive. This allows a clear comparison to the same algorithm used to produce a discriminative classifier. The paper compares the performance of these different variants over multiple data sets and for the full range of misclassification costs and class frequencies. It concludes that although some of these variants are better than a single discriminative classifier, the right choice of training set distribution plus careful calibration are needed to make them competitive with multiple discriminative classifiers.


Support Vector Machine Cost Curve Expected Cost Class Frequency Decision Tree Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chris Drummond
    • 1
  1. 1.Institute for Information TechnologyNational Research Council CanadaOttawa, OntarioCanada

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