|Title||Type I error control for tree classification.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Jung, Sin-Ho, Yong Chen, and Hongshik Ahn|
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 Journal||Cancer Inform|
|Original Publication||Type I error control for tree classification.|
|PubMed Central ID||PMC4237155|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Type I error control for tree classification.