Random survival forest with space extensions for censored data

Publication date: Available online 20 June 2017
Source:Artificial Intelligence in Medicine
Author(s): Hong Wang, Lifeng Zhou
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 techniques, we develop a random survival forest with space extensions algorithm. According to statistical analysis results, we show that the proposed model outperforms or at least comparable to popular survival models such as random survival forest, rotation survival forest, Cox proportional hazard and boosting survival models on well-known benchmark datasets.



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