Making generative classifiers robust to selection bias

  title={Making generative classifiers robust to selection bias},
  author={Andrew T. Smith and Charles Elkan},
This paper presents approaches to semi-supervised learning when the labeled training data and test data are differently distributed. Specifically, the samples selected for labeling are a biased subset of some general distribution and the test set consists of samples drawn from either that general distribution or the distribution of the unlabeled samples. An example of the former appears in loan application approval, where samples with repay/default labels exist only for approved applicants and… CONTINUE READING
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