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Optimization of Fuzzy Rules: Integrated Approach for Classification Problems

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3984))

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Abstract

This paper proposes a GA and GDM-based method for removing the unnecessary rules and generating the relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of the proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. This is achieved by formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules. The fuzzy inference is structured by a set of simple fuzzy rules. In each rule, the antecedent part is made up of the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. In order to prove the effectiveness of the proposed method, computer simulation results are shown.

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References

  1. Lee., K.H., Oh, K.R.: Fuzzy Theory and Applications I-II. Hongrung Press (1991)

    Google Scholar 

  2. Ichihshi, H., Watanabe, T.: Learning control system by a simplified fuzzy reasoning model. In: IPMU 1994, Paris-France, July 2-6, pp. 417–419 (1994)

    Google Scholar 

  3. Abe, S., Lan, M.-S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Transactions on Fuzzy System 3(1), 18–28 (1995)

    Article  MathSciNet  Google Scholar 

  4. Ishibuchi, H., Nozaki, K., Tanaka, H.: Efficient fuzzy partition of pattern space for classification problems. In: Proc. of the Second International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, pp. 671–674 (1992)

    Google Scholar 

  5. lIllshibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems 52, 21–32 (1992)

    Article  Google Scholar 

  6. Ishibuchi, H., Nozaki, K., Weber, R.: Approximate pattern classification with fuzzy boundary. In: Proc. of International Joint Conference on Neural Networks, vol. 52, pp. 21–32 (1992)

    Google Scholar 

  7. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  8. Thrift, P.: Fuzzy logic synthesis with genetic algorithms. In: Proc. of the 12th International Conference on Genetic Algorithm, San Diego, USA, pp. 509–513 (1999)

    Google Scholar 

  9. Karr, C.L.: Design of an adaptive fuzzy logic controller using a genetic algorithm. In: Proc. of the 12th International Conference on Genetic Algorithms, pp. 450–457 (1999)

    Google Scholar 

  10. Karr, C.: Genetic algorithms for fuzzy controllers. AI Expert, 26–33 (February 1998)

    Google Scholar 

  11. Nomura, H., Hayashi, I., Wakami, N.: A self-tuning method of fuzzy control by descent method. In: Proc. of 14th IFSA Congress, Brussels, pp. 155–158 (2001)

    Google Scholar 

  12. Maeda, M., Murakami, S.: Self-tuning fuzzy logic controller. Transactions of the society of Instrument and Control Engineers 34(2), 191–197 (1998)

    Google Scholar 

  13. Carry, H.B.: The method of steepest descent for nonlinear minimization problems. Quart. J. Appl. Math. 2, 258–261 (1994)

    Google Scholar 

  14. Park, D., Kandel, A., Langholz, G.: Genetic-based new fuzzy reasoning models with application to fuzzy control. IEEE Trans.Syst. Man Cybern. 24(1), 39–47 (1994)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronics & Information Engineering, ChonBuk National University, 664-14, 1Ga, DeokJin-Dong, JeonJu, ChonBuk, 561-756, South Korea

    Yunjeong Kang, Malrey Lee & Yongseok Lee

  2. School of Engineering and Technology, National University, 11255 North Torrey Pines Road, La Jolla, CA, 92037, USA

    Thomas M. Gatton

Authors
  1. Yunjeong Kang
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  2. Malrey Lee
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  3. Yongseok Lee
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  4. Thomas M. Gatton
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary,, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd.,, Cathedral House 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Kang, Y., Lee, M., Lee, Y., Gatton, T.M. (2006). Optimization of Fuzzy Rules: Integrated Approach for Classification Problems. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_73

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  • DOI: https://doi.org/10.1007/11751649_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34079-9

  • Online ISBN: 978-3-540-34080-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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