Corpus ID: 10578990

AdaCost: Misclassification Cost-Sensitive Boosting

@inproceedings{Fan1999AdaCostMC,
  title={AdaCost: Misclassification Cost-Sensitive Boosting},
  author={Wei Fan and S. Stolfo and Junxin Zhang and P. Chan},
  booktitle={ICML},
  year={1999}
}
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical evaluations have shown significant reduction in the cumulative misclassification cost over AdaBoost… Expand
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