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Ranking association rules for classification based on genetic network programming

Published: 08 July 2009 Publication History

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

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T. I. R. Agrawal and A. Swami, Mining association rules between sets of items in large databases, In Proc. of the SIGMOD, pages 207--216, 1993.
[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]
S. Mabu, K. Hirasawa and J. Hu, A graph-based evolutionary algorithm: Genetic network programming (gnp) and its extension using reinforcement learning, Evolutionary Computation, 15(3):369--398, 2007.
[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.

Cited By

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  • (2021)MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classificationApplied Intelligence10.1007/s10489-021-02595-wOnline publication date: 29-Jun-2021
  • (2018)Towards an enhanced user's preferences integration into ranking process using dominance approachVietnam Journal of Computer Science10.1007/s40595-017-0098-05:1(15-25)Online publication date: 13-Dec-2018
  • (2011)A new rule ranking model for Associative Classification using a hybrid Artificial Intelligence technique2011 IEEE 3rd International Conference on Communication Software and Networks10.1109/ICCSN.2011.6013816(231-235)Online publication date: May-2011

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      cover image ACM Conferences
      GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
      July 2009
      2036 pages
      ISBN:9781605583259
      DOI:10.1145/1569901

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 08 July 2009

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      Author Tags

      1. association rule
      2. classification
      3. genetic network programming

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      GECCO09
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      GECCO09: Genetic and Evolutionary Computation Conference
      July 8 - 12, 2009
      Québec, Montreal, Canada

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      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      Cited By

      View all
      • (2021)MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classificationApplied Intelligence10.1007/s10489-021-02595-wOnline publication date: 29-Jun-2021
      • (2018)Towards an enhanced user's preferences integration into ranking process using dominance approachVietnam Journal of Computer Science10.1007/s40595-017-0098-05:1(15-25)Online publication date: 13-Dec-2018
      • (2011)A new rule ranking model for Associative Classification using a hybrid Artificial Intelligence technique2011 IEEE 3rd International Conference on Communication Software and Networks10.1109/ICCSN.2011.6013816(231-235)Online publication date: May-2011

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