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Rotation survival forest for right censored data

Recently, survival ensembles have found more and more applications in biological and medical research when censored time-to-event data are often confronted. In this research, we investigate the plausibility of extending a rotation forest, originally proposed for classification purpose, to survival a...

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Detalles Bibliográficos
Autores principales: Zhou, Lifeng, Xu, Qingsong, Wang, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465950/
https://www.ncbi.nlm.nih.gov/pubmed/26082863
http://dx.doi.org/10.7717/peerj.1009
Descripción
Sumario:Recently, survival ensembles have found more and more applications in biological and medical research when censored time-to-event data are often confronted. In this research, we investigate the plausibility of extending a rotation forest, originally proposed for classification purpose, to survival analysis. Supported by the proper statistical analysis, we show that rotation survival forests are able to outperform the state-of-art survival ensembles on right censored data. We also provide a C-index based variable importance measure for evaluating covariates in censored survival data.