Prodding the ROC Curve: Constrained Optimization of Classifier Performance

@inproceedings{Mozer2001ProddingTR,
  title={Prodding the ROC Curve: Constrained Optimization of Classifier Performance},
  author={Michael C. Mozer and Robert H. Dodier and Michael D. Colagrosso and Cesar Guerra-Salcedo and Richard H. Wolniewicz},
  booktitle={NIPS},
  year={2001}
}
When designing a two-alternative classifier, one ordinarily aims to maximize the classifier’s ability to discriminate between members of the two classes. We describe a situation in a real-world business application of machine-learning prediction in which an additional constraint is placed on the nature of the solution: that the classifier achieve a specified correct acceptance or correct rejection rate (i.e., that it achieve a fixed accuracy on members of one class or the other). Our domain is… CONTINUE READING
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