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Transactions on Computational Collective Intelligence V pp 66–83Cite as

Cellular Gene Expression Programming Classifier Learning

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Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 6910))

Abstract

In this paper we propose integrating two collective computational intelligence techniques: gene expression programming and cellular evolutionary algorithms with a view to induce expression trees, which, subsequently, serve as weak classifiers. From these classifiers stronger ensemble classifiers are constructed using majority-voting and boosting techniques. The paper includes the discussion of the validating experiment result confirming high quality of the proposed ensemble classifiers.

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References

  1. Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Springer Science, New York (2008)

    MATH  Google Scholar 

  2. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html

  3. Bi, Y., Guan, J., Bell, D.: The combination of multiple classifiers using an evidentional reasoning approach. Artif. Intell. 172, 1731–1751 (2008)

    Article  MATH  Google Scholar 

  4. Centeno, P., Lawrence, N.D.: Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. Journal of Machine Learning Research 7, 455–491 (2006)

    MATH  MathSciNet  Google Scholar 

  5. Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers, HP Labs Tech Report HPL-2003-4, Palo Alto, Ca (2003)

    Google Scholar 

  6. Ferreira, C.: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)

    MATH  MathSciNet  Google Scholar 

  7. Ferreira, C.: Gene Expression Programming. Studies in Computational Intell. 21, 337–380 (2006)

    Google Scholar 

  8. Freund, Y., Schapire, R.E.: Decision-theoretic generalization of on-line learning and application to boosting. Journal of Computer and System Science 55, 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  9. FT Datasets, http://www.grappa.univ-lille3.fr/torre/Recherche/Experiments/Results/tables.php

  10. Ishibuchi, H., Nojima, Y.: Analysis of Interpretability-accuracy Tradeoff of Fuzzy Systems by Multiobjective Fuzzy Genetics-based Machine Learning. Intern. Journal of Approximate Reasoning 44, 4–31 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  11. Jędrzejowicz, J., Jędrzejowicz, P.: GEP-induced expression trees as weak classifiers. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 129–141. Springer, Heidelberg (2008)

    Google Scholar 

  12. Jędrzejowicz, J., Jędrzejowicz, P.: A family of GEP-induced ensemble classifiers. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 641–652. Springer, Heidelberg (2009)

    Google Scholar 

  13. Jędrzejowicz, J., Jędrzejowicz, P.: Cellular GEP-induced classifiers. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 343–352. Springer, Heidelberg (2010)

    Google Scholar 

  14. Krętowski, M.: A memetic algorithm for global induction of decision trees. In: Geffert, V., Karhumäki, J., Bertoni, A., Preneel, B., Návrat, P., Bieliková, M. (eds.) SOFSEM 2008. LNCS, vol. 4910, pp. 531–540. Springer, Heidelberg (2008)

    Google Scholar 

  15. Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 3, 22–43 (2006)

    Google Scholar 

  16. Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26(5), 1651–1686 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  17. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  18. Torre, F.: Boosting Correct Least General Generalizations, Technical Report GRApp A-0104, Grenoble (2004)

    Google Scholar 

  19. Weinert, W.R., Lopes, H.S.: GEPCLASS: A classification rule discovery tool using gene expression programming. In: Li, X., Zaïane, O.R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 871–880. Springer, Heidelberg (2006)

    Google Scholar 

  20. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  21. Zhou, C., Xiao, W., Tirpak, T.M., Nelson, P.C.: Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation 7(6), 519–531 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Informatics, Gdańsk University, Wita Stwosza 57, 80-952, Gdańsk, Poland

    Joanna Jędrzejowicz

  2. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

Authors
  1. Joanna Jędrzejowicz
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  2. Piotr Jędrzejowicz
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Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Jędrzejowicz, J., Jędrzejowicz, P. (2011). Cellular Gene Expression Programming Classifier Learning. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence V. Lecture Notes in Computer Science, vol 6910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24016-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-24016-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24015-7

  • Online ISBN: 978-3-642-24016-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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