Large-margin learning of Cox proportional hazard models for survival analysis
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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.
KeywordsLarge-margin learning Density estimation Cox proportional hazard models Survival analysis
This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science & Technology).
Compliance with Ethical Standards
Conflict of interests
The authors have no conflict of interest.
Consent for Publication
Consent to submit this manuscript has been received tacitly from the authors’ institution, Seoul National University of Science & Technology.
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