Discriminative vs. Generative Classifiers for Cost Sensitive Learning

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TypeArticle
ConferenceProceedings of the Nineteenth Canadian Conference on Artificial Intelligence, June 7-9, 2006., Quebec City, Quebec, Canada
AbstractThis 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48481
NPARC number8913605
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Record identifier2c5504ab-1c53-4adf-9244-dd723d699bba
Record created2009-04-22
Record modified2016-05-09
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