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Kernel Induced Random Survival Forests (KIRSF) is a statistical learningalgorithm which aims to improve prediction accuracy for survival data. As inRandom Survival Forests (RSF), Cumulative Hazard Function is predicted for eachindividual in the test set. Prediction error is estimated using Harrell'sconcordance index (C index) [Harrell et al. (1982)]. The C-index can beinterpreted as a misclassification probability and does not depend on a singlefixed time for evaluation. The C-index also specifically accounts forcensoring. By utilizing kernel functions, KIRSF achieves better results thanRSF in many situations. In this report, we show how to incorporate kernelfunctions into RSF. We test the performance of KIRSF and compare our method toRSF. We find that the KIRSF's performance is better than RSF in many occasions.

Kernel induced random survival forests