By Topic

A Support Vector Approach to Censored Targets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shivaswamy, P.K. ; Columbia Univ., New York ; Wei Chu ; Jansche, M.

Censored targets, such as the time to events in survival analysis, can generally be represented by intervals on the real line. In this paper, we propose a novel support vector technique (named SVCR) for regression on censored targets. SVCR inherits the strengths of support vector methods, such as a globally optimal solution by convex programming, fast training speed and strong generalization capacity. In contrast to ranking approaches to survival analysis, our approach is able not only to achieve superior ordering performance, but also to predict the survival time very well. Experiments show a significant performance improvement when the majority of the training data is censored. Experimental results on several survival analysis datasets demonstrate that SVCR is very competitive against classical survival analysis models.

Published in:

Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on

Date of Conference:

28-31 Oct. 2007