Proceedings of the Korean Statistical Society Conference (한국통계학회:학술대회논문집)
- 2001.11a
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- Pages.89-94
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- 2001
Robust Variable Selection in Classification Tree
- Jang Jeong Yee (Dept. of Statistics, Pusan National University) ;
- Jeong Kwang Mo (Dept. of Statistics, Pusan National University)
- Published : 2001.11.01
Abstract
In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.