Copula Entropy based Variable Selection for Survival Analysis

TL;DR

This paper introduces a novel variable selection method for survival analysis using Copula Entropy to measure dependence between variables and time-to-event, demonstrating improved interpretability and prediction accuracy.

Contribution

The paper applies Copula Entropy for variable selection in survival analysis, offering a new approach that outperforms existing methods in interpretability and prediction.

Findings

The CE-based method effectively identifies relevant variables.

It outperforms random survival forest and Lasso-Cox in experiments.

Selected variables are more interpretable and predictive.

Abstract

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.