Area-norm COBRA on Conditional Survival Prediction
This work addresses survival analysis for medical or reliability applications, but appears incremental as it builds on existing combined regression strategies.
The paper tackles conditional survival prediction by proposing a combined regression strategy using area between survival curves as a proximity measure, which outperforms Random Survival Forest and includes a novel variable selection technique validated through simulation and three real-life datasets.
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.