Area-norm COBRA on Conditional Survival Prediction

TL;DR

This paper introduces a novel ensemble method for conditional survival prediction using area-norm based combined regression, outperforming Random Survival Forests and including a new variable importance technique.

Contribution

The paper proposes a new ensemble approach for survival analysis based on area-norm proximity, with a novel variable selection method and demonstrated effectiveness through simulations and real datasets.

Findings

Outperforms Random Survival Forests in survival prediction tasks.

Effective variable importance measure in the combined regression framework.

Validated on multiple real-life datasets.

Abstract

The paper explores a different variation of combined regression strategy to calculate the conditional survival function. We use regression based weak learners to create the proposed ensemble technique. The proposed combined regression strategy uses proximity measure as area between two survival curves. The proposed model shows a construction which ensures that it performs better than the Random Survival Forest. The paper discusses a novel technique to select the most important variable in the combined regression setup. We perform a simulation study to show that our proposition for finding relevance of the variables works quite well. We also use three real-life datasets to illustrate the model.