### Abstract

We reduce ranking, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC), to binary classification. The core theorem shows that a binary classification regret of r on the induced binary problem implies an AUC regret of at most 2r. This is a large improvement over approaches such as ordering according to regressed scores, which have a regret transform of r bar right arrow nr where n is the number of elements.

Original language | English |
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Pages (from-to) | 139-153 |

Journal | Machine Learning |

Volume | 72 |

Issue number | 1-2 |

DOIs | |

Publication status | Published - 2008 |

## Cite this

Balcan, M. F., Bansal, N., Beygelzimer, A., Coppersmith, D., Langford, J., & Sorkin, G. B. (2008). Robust reductions from ranking to classification.

*Machine Learning*,*72*(1-2), 139-153. https://doi.org/10.1007/s10994-008-5058-6