Survival analysis is a commonly used technique to identify important
predictors of adverse events and develop guidelines for patient’s treatment
in medical research. When applied to large amounts of patient data, efficient
optimization routines become a necessity. We propose efficient training
algorithms for three kinds of linear survival support vector machines: 1)
ranking-based, 2) regression-based, and 3) combined ranking and regression.
We perform optimization in the primal using truncated Newton optimization and
use order statistic trees to lower computational costs of training. We employ
the same optimization technique and extend it for non-linear models too. Our
results demonstrate the superiority of our proposed optimization scheme over
existing training algorithms, which fail due to their inherently high time
and space complexities when applied to large datasets. We validate the
proposed survival models on 6 real-world datasets, and show that pure
ranking-based approaches outperform regression and hybrid models.
Machine Learning and Knowledge Discovery in Databases