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http://dx.doi.org/10.7232/JKIIE.2014.40.4.375

Rule Selection Method in Decision Tree Models  

Son, Jieun (School of Industrial Management Engineering, Korea University)
Kim, Seoung Bum (School of Industrial Management Engineering, Korea University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.40, no.4, 2014 , pp. 375-381 More about this Journal
Abstract
Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.
Keywords
Classification; Decision tree; Data mining; Rule selection;
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