Turning the hyperparameter of an AUC-optimized classifier

@inproceedings{Tax2005TurningTH,
  title={Turning the hyperparameter of an AUC-optimized classifier},
  author={David M. J. Tax and Cor J. Veenman},
  booktitle={BNAIC},
  year={2005}
}
The Area under the ROC curve (AUC) is a good alternative to the standard empirical risk (classification error) as a performance criterion for classifiers. While most classifier formulations aim at minimizing the classification error, few methods exist that directly optimize the AUC. Moreover, the reported methods that optimize the AUC are often not efficient even for moderately sized datasets. In this paper, we discuss a classifier that optimizes the AUC using a linear programming formulation… CONTINUE READING

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