Adjusting class association rules from global and local perspectives based on evolutionary computation

Authors:
Guangfei Yang;Jiangning Wu;Shingo Mabu;Kaoru Shimada;Kotaro Hirasawa
Affiliations:
Institute of Systems Engineering, Dalian University of Technology, China;Institute of Systems Engineering, Dalian University of Technology, China;Graduate School of Information, Production and Systems, Waseda University, Japan;Graduate School of Information, Production and Systems, Waseda University, Japan;Graduate School of Information, Production and Systems, Waseda University, Japan
Venue:
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Year:
2010

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Abstract

In this paper, we propose an evolutionary method to adjust class association rules from both global and local perspectives. We discover an interesting phenomena that the classification performance could be improved if we import some prior-knowledge, in the form of equations, to re-rank the association rules. We make use of Genetic Network Programming to automatically search the prior-knowledge. In addition to rank the rules globally, we also develop a feedback mechanism to adjust the rules locally, by giving some rewards to good rules and penalties to bad ones. The experimental results on UCI datasets show that the proposed method could improve the classification accuracies effectively.