Copula Entropy based Variable Selection for Survival Analysis
This addresses variable selection for survival analysis in domains like cancer research, but it is incremental as it adapts an existing Copula Entropy method to a new application.
The paper tackled variable selection in survival analysis by applying Copula Entropy to measure correlations between variables and time-to-event, resulting in better prediction performance and more interpretable variable selection compared to random survival forest and Lasso-Cox methods.
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.