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

页码：604 / +

页数：4

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