Robust reductions from ranking to classification

被引:10
|
作者
Balcan, Maria-Florina [1 ]
Bansa, Nikhil [2 ]
Beygelzimer, Alina [3 ]
Coppersmith, Don [4 ]
Langford, John [5 ]
Sorkin, Gregory B. [2 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] IBM Corp, Thomas J. Watson Res Ctr, Yorktown Hts, NY USA
[3] IBM Corp, Thomas J. Watson Res Ctr, Hawthorne, NY USA
[4] IDA, Ctr Commun Res, Princeton, NJ USA
[5] Yahoo Res, New York, NY USA
来源
LEARNING THEORY, PROCEEDINGS | 2007年 / 4539卷
关键词
D O I
10.1007/978-3-540-72927-3_43
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:604 / +
页数:4
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