A Support Vector Approach to Censored Targets
Venue
Seventh IEEE International Conference on Data Mining (ICDM) (2007), pp. 655-660
Publication Year
2007
Authors
Pannagadatta Shivaswamy, Wei Chu, Martin Jansche
BibTeX
Abstract
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.
