  In this paper, we predict conditional survival functions through a combined
regression strategy. We take weak learners as different random survival trees.
We propose to maximize concordance in the right-censored set up to find the
optimal parameters. We explore two approaches, a usual survival cobra and a
novel weighted predictor based on the concordance index. Our proposed
formulations use two different norms, say, Max-norm and Frobenius norm, to find
a proximity set of predictions from query points in the test dataset. We
illustrate our algorithms through three different real-life dataset
implementations.
