@inproceedings{aabef62b00e045b592ecb20e3b6b8e26,

title = "Robust reductions from ranking to classification",

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 $r \mapsto nr$ where n is the number of elements.",

author = "M.F. Balcan and N. Bansal and A. Beygelzimer and D. Coppersmith and J. Langford and G.B. Sorkin",

year = "2007",

doi = "10.1007/978-3-540-72927-3_43",

language = "English",

isbn = "978-3-540-72925-9",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "604--619",

editor = "N.H. Bshouty and C. Gentile",

booktitle = "Learning theory (20th Annual Conference, COLT 2007, San Diego CA, USA, June 13–15, 2007. Proceedings)",

address = "Germany",

}