Large-margin learning of Cox proportional hazard models for survival analysis

Abstract

Machine learning approaches have been recently attempted to tackle the prediction tasks in survival analysis. However, most existing methods aim to learn the prognostic function directly via linear regression or ranking models, unable to exploit the underlying density family, notably the famous CoxPH model. In this paper we propose a novel estimator for the CoxPH model based on the margin maximization principle, which was proven to achieve superb generalization performance in standard classification problems in machine learning. The censored data are effectively handled by incorporating cost-sensitive margin violation loss. We demonstrate the improved prediction performance on several survival datasets.