Cost-Sensitive Learning by Cost-Proportionate Example Weighting

  title={Cost-Sensitive Learning by Cost-Proportionate Example Weighting},
  author={Bianca Zadrozny and John Langford and Naoki Abe},
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conversion is based on cost-proportionate weighting of the training examples, which can be realized either by feeding the weights to the classification algorithm (as often done in boosting), or by careful subsampling. We give some theoretical performance guarantees on the proposed methods, as well as empirical evidence that… CONTINUE READING
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