Some variations on Ensembled Random Survival Forest with application to Cancer Research
Methodology
2018-06-14 v2 Applications
Computation
Machine Learning
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.
Keywords
Cite
@article{arxiv.1709.05515,
title = {Some variations on Ensembled Random Survival Forest with application to Cancer Research},
author = {Arabin Kumar Dey and Suhas N. and Talasila Sai Teja and Anshul Juneja},
journal= {arXiv preprint arXiv:1709.05515},
year = {2018}
}
Comments
16 pages; 10 figures