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Robust reductions from ranking to classification

Balcan, Maria-Florina, Bansal, Nikhil, Beygelzimer, Alina , Coppersmith, Don , Langford, John and Sorkin, Gregory B. (2008) Robust reductions from ranking to classification. Machine Learning, 72 (1-2). pp. 139-153. ISSN 0885-6125

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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.

Item Type: Article
Official URL:
Additional Information: © 2008 Springer Science+Business Media, LLC
Library of Congress subject classification: Q Science > QA Mathematics
Sets: Research centres and groups > Management Science Group
Departments > Management
Date Deposited: 13 Apr 2011 10:52

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