Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection
作者：
Highlights：
• We propose random survival forests for feature extraction for survival analysis.
• We formulate two constraints on the neighborhood graph specific to survival analysis.
• We implement a comparative analysis of 16 feature extraction/selection methods.
• For small sample sizes, models with built-in feature selection are preferred.
• For large sample sizes, feature extraction methods performed comparably.
摘要
Highlights•We propose random survival forests for feature extraction for survival analysis.•We formulate two constraints on the neighborhood graph specific to survival analysis.•We implement a comparative analysis of 16 feature extraction/selection methods.•For small sample sizes, models with built-in feature selection are preferred.•For large sample sizes, feature extraction methods performed comparably.
论文关键词：Feature extraction,Feature selection,Dimensionality reduction,Survival analysis,Censoring,Spectral embedding
论文评审过程：Received 22 February 2016, Revised 15 June 2016, Accepted 25 July 2016, Available online 29 July 2016, Version of Record 5 August 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.07.004
