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Evolutionary Methods for Designing Neuro-fuzzy Modular Systems Combined by Bagging Algorithm

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Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

In this paper we present the problem of designing modular systems combined with the Bagging Algorithm. As component classifiers the Mamdani-type neuro fuzzy-systems are applied and trained using evolutionary methods. Experimental investigations presented in this paper include the classification performed by the modular system built by means of classic Bagging algorithm and its modified version which assigns evolutionary chosen weights to base classifiers.

This work was partly supported by the Foundation for Polish Science (Professorial Grant 2005-2008) and Polish Ministry of Science and Higher Education (Special Research Project 2006-2009, Polish-Singapore Research Project 2008-2010, Research Project 2008-2010).

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References

  1. Arabas, J.: Lectures on Evolutionary Algorithms (in Polish), WNT, Warsaw (2001)

    Google Scholar 

  2. Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)

    Google Scholar 

  3. Breiman, L.: Bagging predictors. Machine Learning 26(2), 123–140 (1996)

    Google Scholar 

  4. Cordon, O., Herrera, F., Hoffman, F., Magdalena, L.: Genetic Fuzzy System. In: Evolutionary Tunning and Learning of Fuzzy Knowledge Bases. World Scientific, Singapore (2000)

    Google Scholar 

  5. Czabanski, R.: Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning. Intenrational Journal of Applied Mathematics and Computer Science 16(3), 357–372 (2006)

    MATH  MathSciNet  Google Scholar 

  6. Duch, W., Korbicz, J., Rutkowski, L., Tadeusiewicz, R.: Biocybernetics and biomedical engineering (in Polish), vol. 6. Akademicka Oficyna Wydawnicza EXIT, Warszawa (2000)

    Google Scholar 

  7. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  8. Gabryel, M., Cpalka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: I Workshop on Genetic Fuzzy Systems, Genewa, pp. 119–123 (2005)

    Google Scholar 

  9. Gabryel, M., Rutkowski, L.: Evolutionary method for learning neuro-fuzzy systems with applications to medical diagnosis (in Polish), XIV Krajowa Konferencja Naukowa Biocybernetyka i Inynieria Biomedyczna, pp. 960-965, Czstochowa (2005)

    Google Scholar 

  10. Gabryel, M., Rutkowski, L.: Evolutionary Learning of Mamdani-Type Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 354–359. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Korytkowski, M., Gabryel, M., Rutkowski, L., Drozda, S.: Evolutionary Methods to Create Interpretable Modular System. LNCS (LNAI), vol. 5097. Springer, Heidelberg (2008)

    Google Scholar 

  12. Kuncheva, L.I.: Fuzzy Classifier Design. Physica Verlag, Heidelberg, New York (2000)

    MATH  Google Scholar 

  13. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  14. Rutkowska, D., Nowicki, D.R.: Implication-Based Neuro-Fuzzy Architectures. International Journal of Applied Mathematics and Computer Science 10(4) (2000)

    Google Scholar 

  15. Rutkowska, D.: Neuro Fuzzy Architectures and Hybrid Learning. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  16. Rutkowski, L.: Computational Inteligence Methods and Techniques (in Polish). PWN, Warszawa (2006)

    Google Scholar 

  17. Rutkowski, L.: Flexible Neuro Fuzzy Systems. Kluwer Academic Publishers, Dordrecht (2004)

    MATH  Google Scholar 

  18. Rutkowski, L.: Methods and Techniques of Artificial Inteligence (in Polish). Wydawnictwo Naukowe PWN, Warsaw (2005)

    Google Scholar 

  19. Mertz, C.J., Murphy, P.M.: UCI respository of machine learning databases, http://www.ics.uci.edu/pub/machine-learning-databases

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Authors and Affiliations

  1. Department of Computer Engineering, Czȩstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czȩstochowa, Poland

    Marcin Gabryel & Leszek Rutkowski

  2. Department of Artificial Intelligence, WSHE University in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Leszek Rutkowski

  3. The Professor Kotarbinski Olsztyn Academy of Computer Science and Management, ul. Artyleryjska 3c, 10-165, Olsztyn, Poland

    Marcin Gabryel

Authors
  1. Marcin Gabryel
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  2. Leszek Rutkowski
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Gabryel, M., Rutkowski, L. (2008). Evolutionary Methods for Designing Neuro-fuzzy Modular Systems Combined by Bagging Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_39

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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