A Study on the Bias Reduction in Split Variable Selection in CART Song, Hyo-Im; Song, Eun-Tae; Song, Moon Sup;
In this short communication we discuss the bias problems of CART in split variable selection and suggest a method to reduce the variable selection bias. Penalties proportional to the number of categories or distinct values are applied to the splitting criteria of CART. The results of empirical comparisons show that the proposed modification of CART reduces the bias in variable selection.
정성석, 김순영, 임한필 (2004), 의사결정나무에서 분리 변수 선택에 관한 연구, [응용통계연구], 제17권, 347-357
Blake, C.L. and Merz, C.J. (1998), UCI repository of machine learning databases (http://www.ics.uc/mleaurnlearn/~MLRepository.html), University of California, Department of Information and Computer Science, Irvine, CA
Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984), Classification and Regression Trees, Chapman and Hall, New York
Dobra, A. and Gehrke, J. (2001), Bias correction in classification tree construction, Proceedings of the Seventeenth International Conference on Machine Learning, 90-97
Kim, H. and Loh, W.Y. (2001), Classification trees with unbiased multiway splits, Journal