Toward personalized care management of patients at risk: the diabetes case study
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Conservative and aggressive rough SVR modeling
Theoretical Computer Science
Support vector methods for survival analysis: a comparison between ranking and regression approaches
Artificial Intelligence in Medicine
Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.