Random survival forest with space extensions for censored data

作者：

Highlights：

• We propose a survival forest based on random forest and space extension technique.

• Our approach outperforms popular survival models such as RSF and Cox models.

• We also provide an R package of the proposed RSFSE algorithm.

摘要

•We propose a survival forest based on random forest and space extension technique.•Our approach outperforms popular survival models such as RSF and Cox models.•We also provide an R package of the proposed RSFSE algorithm.

论文关键词：Survival ensemble,Random forest,Time-to-event data,Censored data,Space extension

论文评审过程：Received 21 August 2016, Revised 7 June 2017, Accepted 9 June 2017, Available online 20 June 2017, Version of Record 12 August 2017.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.06.005