Random survival forests for high-dimensional data
نویسندگان
چکیده
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 and discuss how to properly tune the RF parameters ‘nodesize’ and ‘mtry’. We also introduce new graphical ways of using minimal depth for exploring variable relationships. 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 115–132, 2011
منابع مشابه
Comparison of Survival Forests in Analyzing First Birth Interval
Background and objectives: Application of statistical machine learning methods such as ensemble based approaches in survival analysis has been received considerable interest over the past decades in time-to-event data sets. One of these practical methods is survival forests which have been developed in a variety of contexts due to their high precision, non-parametric and non-linear nature. This...
متن کاملComparison of Random Survival Forests for Competing Risks and Regression Models in Determining Mortality Risk Factors in Breast Cancer Patients in Mahdieh Center, Hamedan, Iran
Introduction: Breast cancer is one of the most common cancers among women worldwide. Patients with cancer may die due to disease progression or other types of events. These different event types are called competing risks. This study aimed to determine the factors affecting the survival of patients with breast cancer using three different approaches: cause-specific hazards regression, subdistri...
متن کاملHigh-Dimensional Variable Selection for Survival Data
The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a...
متن کاملPathway hunting by random survival forests
MOTIVATION Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but...
متن کاملEvaluating Random Forests for Survival Analysis using Prediction Error Curves.
Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error curves. The software implements inverse probability of censoring weights to deal with right censored data and several variants of cross-validation to deal with the apparent err...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Statistical Analysis and Data Mining
دوره 4 شماره
صفحات -
تاریخ انتشار 2011