Ranking association rules for classification based on genetic network programming

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Ranking association rules for classification based on genetic network programming

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

POSTER SESSION: Track 11: genetics-based machine learning table of contents

Pages 1917-1918

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Guangfei Yang	Waseda University, Kitakyushu, Japan
Shingo Mabu Mabu	Waseda University, Kitakyushu, Japan
Kaoru Shimada	Waseda University, Kitakyushu, Japan
Yunlu Gong	Waseda University, Kitakyushu, Japan
Kotaro Hirasawa	Waseda University, Kitakyushu, Japan

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
	2	B. Liu, W. Hsu and Y. Ma, Integrating classification and association rule mining, In Proc. of the KDD, pages 80--86, 1998.
	3	G. Yang, K. Shimada, S. Mabu and K. Hirasawa, A nonlinear model to rank association rules based on semantic similarity and genetic network programming, IEEJ Trans. on Electrical and Electronic Engineering, 4(1):1--9, 2008.
	4	Shingo Mabu , Kotaro Hirasawa , Jinglu Hu, A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning, Evolutionary Computation, v.15 n.3, p.369-398, Fall 2007 [doi>10.1162/evco.2007.15.3.369]
	5	S. Mabu, K. Hirasawa, Y. Matsuya and J. Hu, Genetic Network Programming for Automatic Program Generation, J. of Advanced Computational Intelligence and Intelligent Informatics, 9(4):430--435, 2005.
	6	F. Coenen, LUCS KDD implementation of CBA, Department of Computer Science, The University of Liverpool, UK, 2004.