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Ordinal Regression by Extended Binary Classification

Li, Ling and Lin, Hsuan-Tien (2007) Ordinal Regression by Extended Binary Classification. In: Advances in Neural Information Processing Systems 19 (NIPS 2006). Advances in Neural Information Processing Systems. No.19. MIT Press , Cambridge, MA, pp. 865-872. ISBN 0-262-19568-2.

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We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranking rule from the binary classifier. A weighted 0/1 loss of the binary classifier would then bound the mislabeling cost of the ranking rule. Our framework allows not only to design good ordinal regression algorithms based on well-tuned binary classification approaches, but also to derive new generalization bounds for ordinal regression from known bounds for binary classification. In addition, our framework unifies many existing ordinal regression algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms, which demonstrates the usefulness of our framework.

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Additional Information:We wish to thank Yaser S. Abu-Mostafa, Amrit Pratap, John Langford, and the anonymous reviewers for valuable discussions and comments. Ling Li was supported by the Caltech SISL Graduate Fellowship, and Hsuan-Tien Lin was supported by the Caltech EAS Division Fellowship.
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Caltech SISL Graduate FellowshipUNSPECIFIED
Caltech EAS Division FellowshipUNSPECIFIED
Caltech Social and Information Sciences LaboratoryUNSPECIFIED
Series Name:Advances in Neural Information Processing Systems
Issue or Number:19
Record Number:CaltechAUTHORS:20160315-111243621
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65362
Deposited By: Kristin Buxton
Deposited On:30 Mar 2016 23:44
Last Modified:03 Oct 2019 09:46

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