A new associative classification method by integrating CMAR and RuleRank model based on Genetic Network Programming - Guangfei Yang; Mabu, S.; Shimada, K.; Hirasawa, K. (2009 ICCAS-SICE : 18-21 August 2009, 2009)
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.