Abstract
Survival analysis is a fundamental tool in medical research to identify
predictors of adverse events and develop systems for clinical decision
support. In order to leverage large amounts of patient data, efficient
optimisation routines are paramount. We propose an efficient training
algorithm for the kernel survival support vector machine (SSVM). We directly
optimise the primal objective function% (without simplifying it) and employ
truncated Newton optimisation and order statistic trees to significantly
lower computational costs compared to previous training algorithms, which
require $O(n^4)$ space and $O(p n^6)$ time for datasets with $n$ samples and
$p$ features. Our results demonstrate that our proposed optimisation scheme
allows analysing data of a much larger scale with no loss in prediction
performance. Experiments on synthetic and 5 real-world datasets show that our
technique outperforms existing kernel SSVM formulations if the amount of
right censoring is high ($≥85%$), and performs comparably otherwise.
Publication
3rd Workshop on Machine Learning in Life Sciences