Random survival forest with space extensions for censored data

@article{Wang2017RandomSF,
  title={Random survival forest with space extensions for censored data},
  author={Hong Wang and Lifeng Zhou},
  journal={Artificial intelligence in medicine},
  year={2017},
  volume={79},
  pages={52-61}
}
Prediction capability of a classifier usually improves when it is built from an extended variable space by adding new variables from randomly combination of two or more original variables. However, its usefulness in survival analysis of censored time-to-event data is yet to be verified. In this research, we investigate the plausibility of space extension technique, originally proposed for classification purpose, to survival analysis. By combing random subspace, bagging and extended space… CONTINUE READING