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Cancer Informatics

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Type I Error Control for Tree Classification

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Publication Date: 16 Nov 2014

Type: Review

Journal: Cancer Informatics

Citation: Cancer Informatics 2014:Suppl. 7 11-18

doi: 10.4137/CIN.S16342

Abstract

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%.


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