A new associative classification method by integrating CMAR and RuleRank model based on Genetic Network Programming

In this paper, we propose an evolutionary approach to rank association rules for classification. The association rules are ranked by their support, confidence and length in one of the most important associative classification method, Classification based on Multiple Association Rule (CMAR). However,...

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Bibliographic Details
Published in2009 ICCAS-SICE : 18-21 August 2009 pp. 3874 - 3879
Main Authors Guangfei Yang, Mabu, S., Shimada, K., Hirasawa, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Association rules
Data mining
Electronic mail
Equations
Genetic programming
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ISBN9784907764340
4907764340

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Summary:In this paper, we propose an evolutionary approach to rank association rules for classification. The association rules are ranked by their support, confidence and length in one of the most important associative classification method, Classification based on Multiple Association Rule (CMAR). However, from some empirical studies, we find that if the rules are ranked by some equations first, the classification accuracy will be improved in some data sets. In order to generate such equations effectively, we propose a RuleRank model based on genetic network programming (GNP). The experimental results show that our method could improve the classification accuracies effectively.
ISBN:9784907764340
4907764340