Jung Sin-Ho, Chen Yong, Ahn Hongshik
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.
Cancer Inform. 2014 Nov 16;13(Suppl 7):11-8. doi: 10.4137/CIN.S16342. eCollection 2014.
Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe type I error probability. Nonetheless, there have not been many publications to address this issue. In this paper, we propose a binary tree classification method to control the probability to accept a predictor below certain level, say 5%.
二叉树分类法已被用于根据与所选预测变量相关的结果变量水平对总体进行分类。通常，我们在进行分类时会有大量的候选预测变量，并且每个预测变量都有多个不同的截断值。由于存在这类多重性问题，二叉树分类方法容易出现严重的I型错误概率。尽管如此，针对这一问题的相关出版物并不多。在本文中，我们提出了一种二叉树分类方法，以控制接受某个预测变量的概率低于特定水平（如5%）。
