Piecewise-linear criterion functions in oblique survival tree induction
作者：
Highlights：
• I proposed an oblique survival tree that is able to cope with right-censored data.
• The induction algorithm is based on the minimization of the CPL criterion functions.
• A split-complexity measure with a 10-fold cross-validation was used to prune a tree.
• The predictive ability of the method was evaluated using synthetic and real data.
• The outcomes were compared with two existing univariate tree models.
• The obtained survival trees are small and offer a good predictive ability.
摘要
Highlights•I proposed an oblique survival tree that is able to cope with right-censored data.•The induction algorithm is based on the minimization of the CPL criterion functions.•A split-complexity measure with a 10-fold cross-validation was used to prune a tree.•The predictive ability of the method was evaluated using synthetic and real data.•The outcomes were compared with two existing univariate tree models.•The obtained survival trees are small and offer a good predictive ability.
论文关键词：Piecewise-linear criterion function,Survival tree,Oblique splits,Right-censored data
论文评审过程：Received 15 July 2016, Revised 7 November 2016, Accepted 28 December 2016, Available online 3 January 2017, Version of Record 8 January 2017.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.12.004
