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ICML
2004
IEEE

Learning and evaluating classifiers under sample selection bias

11 years 15 days ago
Learning and evaluating classifiers under sample selection bias
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions. In many practical situations, however, this assumption is violated, in a problem known in econometrics as sample selection bias. In this paper, we formalize the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it. We also present a bias correction method that is particularly useful for classifier evaluation under sample selection bias.
Bianca Zadrozny
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Bianca Zadrozny
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