Concordance based Survival Cobra with regression type weak learners
This addresses survival analysis for medical or reliability applications, but appears incremental as it builds on existing survival cobra methods with new weighting and norm variations.
The paper tackles survival function prediction by combining random survival trees and maximizing concordance for right-censored data, proposing two approaches including a weighted predictor based on concordance index. Results show implementations on three real-life datasets, though no specific performance numbers are provided.
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.