Volume 72, Numbers 1-2, 139-153, DOI: 10.1007/s10994-008-5058-6

Robust reductions from ranking to classification

Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford and Gregory B. Sorkin

From the issue entitled "Special issue on Learning Theory (COLT-2007); Guest Editors: Claudio Gentile, Nader H. Bshouty"

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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 nr where n is the number of elements.

Keywords  Ranking - Classification - Reductions

Editors: Claudio Gentile, Nader H. Bshouty.

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