Random survival forests for high‐dimensional data

Statistical Analysis and Data Mining

Udaya B. Kogalur¹  Andy J. Minn¹  Xi Chen⁴  Hemant Ishwaran⁴ 

[1] 01, Cleveland Clinic, Cleveland OH 44195;6848;Dept of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232‐Dept of Quantitative Health Sciences JJN3‐Dept of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104

关键词: forests;    maximal subtree;    minimal depth;    trees;    variable selection;    VIMP;   

DOI  :  10.1002/sam.10103

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Abstract Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problems using Random Survival Forests (RSF), a new extension of Breiman's Random Forests (RF) to survival settings. We review this methodology and demonstrate its use in high-dimensional survival problems using a public domain R-language package randomSurvivalForest. We discuss effective ways to regularize forests an.

【 授权许可】

Unknown