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 |
Journal | Cancer Inform |
Volume | 13 |
Issue | Suppl 7 |
Pagination | 11-8 |
Date Published | 2014 |
ISSN | 1176-9351 |
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%. |
DOI | 10.4137/CIN.S16342 |
Alternate Journal | Cancer Inform |
Original Publication | Type I error control for tree classification. |
PubMed ID | 25452689 |
PubMed Central ID | PMC4237155 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States |
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