Ranking association rules for classification based on genetic network programming

Authors:
Guangfei Yang;Shingo Mabu Mabu;Kaoru Shimada;Yunlu Gong;Kotaro Hirasawa
Affiliations:
Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 2
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Abstract

In this paper, we propose a Genetic Network Programming (GNP) based ranking method to improve the accuracy of Classification Based on Association Rule(CBA). We start from an empirical phenomenon, that is, the accuracy could be improved by changing the ranking of rules in CBA. Then, we apply GNP to build a model, namely RuleRank, to find good ranking equations to rank association rules in CBA. The simulation results show that RuleRank could improve the accuracy of CBA effectively.