Open AccessA Selective Review on Random Survival Forests for High Dimensional Data
Author(s) -
Hong Wang,
Gang Li
Publication year - 2017
Publication title -
quantitative bio-science
Language(s) - English
Resource type - Journals
eISSN - 2508-7185
pISSN - 2288-1344
DOI - 10.22283/qbs.2017.36.2.85
Subject(s) - random forest , covariate , event (particle physics) , survival analysis , computer science , data science , event data , variety (cybernetics) , random effects model , parametric statistics , machine learning , statistics , artificial intelligence , mathematics , medicine , meta analysis , physics , quantum mechanics
Over the past decades, there has been considerable interest in applying statistical machine learning methods in survival analysis. Ensemble based approaches, especially random survival forests, have been developed in a variety of contexts due to their high precision and non-parametric nature. This article aims to provide a timely review on recent developments and applications of random survival forests for time-to-event data with high dimensional covariates. This selective review begins with an introduction to the random survival forest framework, followed by a survey of recent developments on splitting criteria, variable selection, and other advanced topics of random survival forests for time-to-event data in high dimensional settings. We also discuss potential research directions for future research.