Some variations on Ensembled Random Survival Forest with application to Cancer Research
This work addresses survival prediction in cancer research, but appears incremental as it builds on existing ensemble methods.
The paper tackles survival function prediction by implementing a novel AdaBoost method with variations, comparing them on runtime and root mean square error across datasets including right censoring and competing risk data, but no concrete numbers are provided in the abstract.
In this paper we describe a novel implementation of adaboost for prediction of survival function. We take different variations of the algorithm and compare the algorithms based on system run time and root mean square error. Our construction includes right censoring data and competing risk data too. We take different data set to illustrate the performance of the algorithms.