  Variable selection is an important problem in statistics and machine
learning. Copula Entropy (CE) is a mathematical concept for measuring
statistical independence and has been applied to variable selection recently.
In this paper we propose to apply the CE-based method for variable selection to
survival analysis. The idea is to measure the correlation between variables and
time-to-event with CE and then select variables according to their CE value.
Experiments on simulated data and two real cancer data were conducted to
compare the proposed method with two related methods: random survival forest
and Lasso-Cox. Experimental results showed that the proposed method can select
the 'right' variables out that are more interpretable and lead to better
prediction performance.
