Severe Class Imbalance: Why Better Algorithms Aren't the Answer

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ConferenceProceedings of the 16th European Conference of Machine Learning, October 3-7, 2005., Porto, Portugal
AbstractThis paper argues that severe class imbalance is not just an interesting technical challenge that improved learning algorithms will address, it is much more serious. To be useful, a classifier must appreciably outperform a trivial solution, such as choosing the majority class. Any application that is inherently noisy limits the error rate, and cost, that is achievable. When data are normally distributed, even a Bayes optimal classifier has a vanishingly small reduction in the majority classifier's error rate, and cost, as imbalance increases. For fat tailed distributions, and when practical classifiers are used, often no reduction is achieved.
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AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48258
NPARC number9190916
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Record identifierf0b7a37b-d7c5-470c-b6e5-eb7321281478
Record created2009-06-30
Record modified2016-05-09
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