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Multivariate Decision Tree for High -dimensional Response Vector with Its Application
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 Title & Authors
Multivariate Decision Tree for High -dimensional Response Vector with Its Application
Lee, Seong-Keon;
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 Abstract
Multiple responses are often observed in many application fields, such as customer`s time-of-day pattern for using internet. Some decision trees for multiple responses have been constructed by many researchers. However, if the response is a high-dimensional vector that can be thought of as a discretized function, then fitting a multivariate decision tree may be unsuccessful. Yu and Lambert (1999) suggested spline tree and principal component tree to analyze high dimensional response vector by using dimension reduction techniques. In this paper, we shall propose factor tree which would be more interpretable and competitive. Furthermore, using Korean internet company data, we will analyze time-of-day patterns for internet user.
 Keywords
Factor tree;High-dimensional response;Multivariate decision tree;Principal component tree;Spline tree;
 Language
English
 Cited by
1.
A Study on the Estimating of the Number of Principal Components using Modified RV-Coefficient,;;

Journal of the Korean Data Analysis Society, 2011. vol.13. 5, pp.2219-2226
 References
1.
Breiman, L., Friedman, J H., Olshen, R. A, Stone, C. J (1984), Classification and Regression Trees, Wadsworth, CA

2.
Green, P. J, Silverman, B. W. (1999), Nonparametric Regression and generalized Linear Models, Chapman and Hall, London

3.
Johnson, R. A, Wichern, D. W. (1998), Applied Multivariate Statistical Analysis, Prentice-Hall, London