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Variable selection in the kernel Cox regression  

Shim, Joo-Yong (Department of Data Science, Inje University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.4, 2011 , pp. 795-801 More about this Journal
Abstract
In machine learning and statistics it is often the case that some variables are not important, while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables in the kernel Cox regression. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of relevant variables in the kernel Cox regression. Experimental results are then presented which indicate the performance of the proposed method.
Keywords
ANOVA decomposition kernel; generalized cross validation function; kernel Cox regression model; variable selection;
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Times Cited By KSCI : 4  (Citation Analysis)
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