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A Study on Multivariate Oblique Decision Tree - Bivariate Linear Combination Split -
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다변량 사각 의사결정나무에 관한 연구 - 이변량 분리기준을 중심으로 -

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Type
Academic journal
Author
Journal
The Korean Data Analysis Society Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제12권 제1호 KCI Accredited Journals
Published
2010.1
Pages
251 - 260 (10page)

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A Study on Multivariate Oblique Decision Tree - Bivariate Linear Combination Split -
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Abstract· Keywords

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A classification tree is a rule for predicting the class of an object from the values for its predictor variables. The common goal in CART, CHAID, C4.5 and QUEST is to obtain such that in each terminal node is quite pure and simple tree. Occasionally this cannot be achieved with standard algorithms can produce large tree structure because they use only single splits. This study introduce a classification tree split criterion that can improve class prediction using linear combination split. We focus on bivariate linear combination splits in this study. Our splits are simple bivariate linear combination split and golden section splits. Some simulation and real data experiments are performed to demonstrate the performance of the proposed approach.
#의사결정나무 #선형결합분리 #이변량분리 #황금비율 #decision tree #linear combination split #bivariate split #golden section split

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References (10)

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Breiman, J. / 1984 / Classification and regression trees / Wadsworth google schola Kass, G. / 1980 / An Exploratory technique for investigating large quantities of categorical data / Applied Statistics 29 : 119 ~ 129 google schola Kim, H. / 2001 / Classification trees with unbiased multiway splits / Journal of the American Statistical Association 96 : 589 ~ 604 google schola Kim, H. / 2003 / Classification Trees with Bivariate Linear Discriminant Node Models / Journal of the Computational and Graphical Statistics 12 : 512 ~ 530 google schola Loh, W. Y. / 1997 / Split Selection Methods for Classification Trees / Statistica Sinica 7 : 815 ~ 840 google schola

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