Random rotation survival forest for high dimensional censored data.
Recently, rotation forest has been extended to regression and survival analysis problems. However, due to intensive computation incurred by principal component analysis, rotation forest often fails when high-dimensional or big data are confronted. In this study, we extend rotation forest to high dimensional censored time-to-event data analysis by combing random subspace, bagging and rotation forest. Supported by proper statistical analysis, we show that the proposed method random rotation survival forest outperforms state-of-the-art survival ensembles such as random survival forest and popular regularized Cox models.
School of Mathematics and Statistics, Central South University, South Shaoshan Road, Changsha, 410075 Hunan China.
Lifeng
L
Zhou
School of Mathematics and Statistics, Central South University, South Shaoshan Road, Changsha, 410075 Hunan China.
Hong
H
Wang
School of Mathematics and Statistics, Central South University, South Shaoshan Road, Changsha, 410075 Hunan China.
Qingsong
Q
Xu
Censored data; High-dimensional data; Rotation forest; Survival ensemble; Time-to-event data