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Improved Decision Tree Algorithms by Considering Variables Interaction  

Kwon, Keunseob (Department of Industrial Engineering, Hanyang University)
Choi, Gyunghyun (Department of Industrial Engineering, Hanyang University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.30, no.4, 2004 , pp. 267-276 More about this Journal
Abstract
Much of previous attention on researches of the decision tree focuses on the splitting criteria and optimization of tree size. Nowadays the quantity of the data increase and relation of variables becomes very complex. And hence, this comes to have plenty number of unnecessary node and leaf. Consequently the confidence of the explanation and forecasting of the decision tree falls off. In this research report, we propose some decision tree algorithms considering the interaction of predictor variables. A generic algorithm, the k-1 Algorithm, dealing with the interaction with a combination of all predictor variable is presented. And then, the extended version k-k Algorithm which considers with the interaction every k-depth with a combination of some predictor variables. Also, we present an improved algorithm by introducing control parameter to the algorithms. The algorithms are tested by real field credit card data, census data, bank data, etc.
Keywords
decision tree; data mining; interaction;
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