Abstract
For function approximation using fuzzy if-then rules, Nomura et al. (1992) proposed a genetic algorithm-based method for adjusting the fuzzy partition of an input space. In this paper, we apply their method to pattern classification problems. We have already extended the coding method to the case where intervals and trapezoidal membership functions can be used for antecedent fuzzy sets (Ishibuchi & Murata, 1996). There are, however, two drawbacks in these methods. One is that the resolution of each axis on which the fuzzy partition is adjusted should be prespecified by a decision-maker. The other is that the number of fuzzy if-then rules exponentially increases as the number of attributes increases. To cope with these drawbacks, we propose a genetic algorithmbased fuzzy partition method that has the following advantages;
-
1.
The resolution of each axis is determined by the histogram of training patterns.
-
2.
The membership function is determined by the histogram and a genetic algorithm.
-
3.
Input selection is also performed by the genetic algorithm.
We show the effectiveness of the proposed method by computer simulations on iris data with 4 attributes and wine data with 13 attributes.
Preview
Unable to display preview. Download preview PDF.
References
J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, 1989.
M. Gen and R. Cheng, Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc., New York, 1996.
C. L. Karr, “Design of an adaptive fuzzy logic controller using a genetic algorithm,” Proc. 4th ICGA, R.K. Belew and L.B. Booker Eds., Morgan Kaufmann Publishers, San Mateo, CA, pp. 450–457, 1991.
C. L. Karr and E. J. Gentry, “Fuzzy control of PH using genetic algorithms,” IEEE Trans. on Fuzzy Systems, vol. 1, no. 1, pp. 46–53, 1993.
H. Nomura et al., “A self-tuning method of fuzzy reasoning by genetic algorithm,” Proc. International Fuzzy Systems and Intelligent Control Conference, pp. 236–245, 1992.
H. Nomura and N. Wakami, “A method to determine fuzzy inference rules by a genetic algorithm,” Trans. of Institute of Electronics, Information and Communication Engineers A, vol. J77-A, no. 9, pp. 1241–1249, 1994 (in Japanese).
T. Fukuda et al., “Structure optimization of fuzzy neural network by genetic algorithm,” Proc. 5th IFSA Congress, pp. 964–967, 1993.
P. Thrift, “Fuzzy logic synthesis with genetic algorithms,” Proc. 4th ICGA, R.K. Belew and L.B. Booker Eds., Morgan Kaufmann Publishers, San Mateo, CA, pp. 509–513, 1991.
M. Valenzuela-Rendon, “The fuzzy classifier system: A classifier system for continuously varying variables,” Proc. 4th ICGA, R.K. Belew and L.B. Booker Eds., Morgan Kaufmann Publishers, San Mateo, CA, pp. 346–353, 1991.
Furuhashi et al., “Suppression of excessive fuzziness using multiple fuzzy classifier systems,” Proc. FUZZ-IEEE'94, IEEE Service Center, Piscataway, NJ, pp. 411–414, 1994.
H. Ishibuchi et al., “A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems,” Proc. ICEC'95, IEEE Service Center, Piscataway, NJ, pp. 759–764, 1995.
H. Ishibuchi and T. Murata, “A genetic-algorithm-based fuzzy partition method for pattern classification problems,” Genetic Algorithms and Soft Computing (Studies in Fuzziness, Vol.8), Phisyca-Verlag, Heidelberg, pp. 555–578, 1996.
M. Forina et al., “Wine recognition database,” available via anonymous ftp from ics.uci.edu in directory /pub/machine-leaning-database/wine, 1992.
T. Nakashima et al., “Input selection in fuzzy rule-based classification systems,” Proc. FUZZ-IEEE'97, IEEE Service Center, Piscataway, NJ, pp. 1457–1462, 1997.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Murata, T., Ishibuchi, H., Nakashima, T., Gen, M. (1998). Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040793
Download citation
DOI: https://doi.org/10.1007/BFb0040793
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64891-8
Online ISBN: 978-3-540-68515-9
eBook Packages: Springer Book Archive