CaltechAUTHORS
  A Caltech Library Service

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. https://resolver.caltech.edu/CaltechAUTHORS:20160315-111243621

[img] PDF - Published Version
See Usage Policy.

168Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20160315-111243621

Abstract

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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://papers.nips.cc/paper/3125-ordinal-regression-by-extended-binary-classificationOrganizationPaper
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.
Funders:
Funding AgencyGrant Number
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
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160315-111243621
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65362
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:30 Mar 2016 23:44
Last Modified:03 Oct 2019 09:46

Repository Staff Only: item control page