Variable selection in the kernel Cox regression

  • Received : 2011.05.29
  • Accepted : 2011.06.26
  • Published : 2011.08.01


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.


Supported by : NRF


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