Random forest of dipolar trees for survival prediction

被引：0
|
作者：
Kretowska, Malgorzata [1 ]
机构：
[1] Bialystok Tech Univ, Fac Comp Sci, PL-15351 Bialystok, Poland
来源：
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2006, PROCEEDINGS | 2006年 / 4029卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the paper the method of using the ensemble of dipolar trees for survival prediction is presented. In the approach the random forest is applied to calculate the aggregated Kaplan-Meier survival function for a new patient. The induction of individual dipolar regression tree is based on minimization of a piece-wise linear criterion function. The algorithm allows using the information from censored observations for which the exact survival time is unknown. The Brier score is used to evaluate the prediction ability of the received model.
引用
收藏
页码：909 / 918
页数：10
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