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International Symposium on Evolutionary Computation

International Symposium on Swarm Intelligence and Differential Evolution

EC 2012, SIDE 2012: Swarm and Evolutionary Computation pp 395–403Cite as

reCORE – A Coevolutionary Algorithm for Rule Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7269))

Abstract

A coevolutionary algorithm called reCORE for rule extraction from databases was proposed in the paper. Knowledge is extracted in the form of simple implication rules IF-THEN-ELSE. There are two populations involved, one for rules and second for rule sets. Each population has a distinct evolution scheme. One individual from rule set population and a number of individuals from rule population contribute to final result. Populations are synchronized to keep the cross-references up to date. The concept of the algorithm and the results of comparative research are presented. A cross-validation is used for examination, and the final conclusions are drawn based upon statistically analyzed results. They state that, the effectiveness of the reCORE algorithm is comparable to other rule classifiers, such as Ridor or JRip.

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References

  1. Frank, A., Asuncion, A.: UCI machine learning repository (2010)

    Google Scholar 

  2. Freitas, A.A.: Book review: Data mining using grammar-based genetic programming and applications. Genetic Programming and Evolvable Machines 2, 197–199 (2001), doi:10.1023/A:1011564616547

    Article  Google Scholar 

  3. Giordana, A., Saitta, L., Zini, F.: Learning disjunctive concepts with distributed genetic algorithms. In: International Conference on Evolutionary Computation 1994, pp. 115–119 (1994)

    Google Scholar 

  4. Greene, D.P., Smith, S.F.: Competition-based induction of decision models from examples. Mach. Learn. 13, 229–257 (1993)

    Article  Google Scholar 

  5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)

    Article  Google Scholar 

  6. Holland, J.H.: Outline for a logical theory of adaptive systems. J. ACM 9, 297–314 (1962)

    Article  MATH  Google Scholar 

  7. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. A Bradford Book (April 1992)

    Google Scholar 

  8. Janikow, C.Z.: A knowledge-intensive genetic algorithm for supervised learning. Mach. Learn. 13, 189–228 (1993)

    Article  Google Scholar 

  9. Jourdan, L., Basseur, M., Talbi, E.-G.: Hybridizing exact methods and metaheuristics: A taxonomy. European Journal of Operational Research 199(3), 620–629 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Myszkowski, P.B.: Coevolutionary algorithm for rule induction. In: IMCSIT, pp. 73–79 (2010)

    Google Scholar 

  11. Myszkowski, P.B.: Rule Induction Based-On Coevolutionary Algorithms for Image Annotation. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS, vol. 6592, pp. 232–241. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Smith, S.F.: Flexible learning of problem solving heuristics through adaptive search. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, vol. 1, pp. 422–425. Morgan Kaufmann Publishers Inc., San Francisco (1983)

    Google Scholar 

  13. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Information Processing & Management 45(4), 427–437 (2009)

    Article  Google Scholar 

  14. Tan, K.C., Yu, Q., Ang, J.H.: A coevolutionary algorithm for rules discovery in data mining. International Journal of Systems Science 37(12), 835–864 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Venturini, G.: SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts, pp. 280–296 (1993)

    Google Scholar 

  16. Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3, 149–175 (1995)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Instititute of Informatics, Wroclaw University of Technology, Wybrzeże Wyspiańskiego 27, 50-350, Wrocław, Poland

    Bogdan Trawiński & Grzegorz Matoga

Authors
  1. Bogdan Trawiński
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  2. Grzegorz Matoga
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hallz, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Trawiński, B., Matoga, G. (2012). reCORE – A Coevolutionary Algorithm for Rule Extraction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_46

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  • DOI: https://doi.org/10.1007/978-3-642-29353-5_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29352-8

  • Online ISBN: 978-3-642-29353-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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