Cargando…
A comparative study of forest methods for time-to-event data: variable selection and predictive performance
BACKGROUND: As a hot method in machine learning field, the forests approach is an attractive alternative approach to Cox model. Random survival forests (RSF) methodology is the most popular survival forests method, whereas its drawbacks exist such as a selection bias towards covariates with many pos...
Autores principales: | Liu, Yingxin, Zhou, Shiyu, Wei, Hongxia, An, Shengli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465777/ https://www.ncbi.nlm.nih.gov/pubmed/34563138 http://dx.doi.org/10.1186/s12874-021-01386-8 |
Ejemplares similares
-
Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes
por: Pi, Lira, et al.
Publicado: (2018) -
Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings
por: Gilhodes, Julia, et al.
Publicado: (2020) -
Evaluation of variable selection methods for random forests and omics data sets
por: Degenhardt, Frauke, et al.
Publicado: (2017) -
Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables
por: Kaneko, Hiromasa
Publicado: (2021) -
Forward variable selection for random forest models
por: Velthoen, Jasper, et al.
Publicado: (2022)