Type I error control for tree classification.

TitleType I error control for tree classification.
Publication TypeJournal Article
Year of Publication2014
AuthorsJung, Sin-Ho, Yong Chen, and Hongshik Ahn
JournalCancer Inform
IssueSuppl 7
Date Published2014

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%.

Alternate JournalCancer Inform
Original PublicationType I error control for tree classification.
PubMed ID25452689
PubMed Central IDPMC4237155
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States