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Variable Selection with Regression Trees
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 Title & Authors
Variable Selection with Regression Trees
Chang, Young-Jae;
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 Abstract
Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.
 Keywords
Regression tree;random forest;variable selection;bagging;
 Language
English
 Cited by
1.
Multi-Step Classification Trees, Communications in Statistics - Simulation and Computation, 2012, 41, 9, 1728  crossref(new windwow)
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