International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지

Identification of a Gaussian Fuzzy Classifier

  • Heesoo Hwang (School of Electrical Engineering, Halla University)
  • Published : 2004.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.

Keywords

References

  1. Intelligent Engineering Systems through Artificial Neural Networks v.9 Semi-supervised scaled clustering using genetic algorithms A. Demiriz;K. P. Bennett;M. J. Embrechts
  2. Proc. of the First IEEE Conference on Evolutionary Computation v.1 Using real-valued genetic algorithm to evolve rule sets for classification A. L. Corcoran;S. Sen
  3. Proc. of the First IEEE Conference on Evolutionary Computation v.1 Fuzzy clustering with genetic search B. P. Buckles;F. E. Petry;D. Prabhu;R. George;R. Srikanth
  4. Proc. of the Fourth IEEE International Conference on Fuzzy Systems v.4 Evolutionary fuzzy C-means clustering algorithm B. Yuan;G. J. Klir;J. F. Swan-Stone https://doi.org/10.1109/FUZZY.1995.409988
  5. Artificial Intelligence in Medicine v.16 Obtaining interpretable fuzzy classification rules from medical data D. Mauck;R. Kruse https://doi.org/10.1016/S0933-3657(98)00070-0
  6. IEEE Trans. on Systems, Man and Cybernetics v.29 no.5 Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems H. Ishibuchi;T. Nakashima;T. Murata https://doi.org/10.1109/3477.790443
  7. IEEE Trans. on Fuzzy Systems v.9 no.4 Compact and transparent fuzzy models and classifiers through iterative complexity reduction H. Roubos;M. Setnes https://doi.org/10.1109/91.940965
  8. Proc. Recent Advances in Soft Computing Learning fuzzy classification rules from data H. Roubos;M. Setnes;J. Abonyi
  9. IEEE Trans. on Systems, Man and Cybernetics v.29 no.1 Semantic constraints for membership function optimization J. V. D. Oliveira https://doi.org/10.1109/3468.736369
  10. Proc. of the Third IEEE Conference on Fuzzy Systems v.1 Trainable fuzzy classification systems based on fuzzy if-then rules K. Nozaki;H. Ishibuchi;H. Tanaka
  11. IEEE Trans. on System, Man and Cybernetics v.28 no.1 Nearest prototype classification: clustering, genetic algorithm, or random search L. I. Kuncheva;J. C. Bezdek https://doi.org/10.1109/5326.661099
  12. Proc. of the First International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference Genetic fuzzy clustering L. O. Hall;J. C. Bezdek;S. Boggavarapu;A. Bensaid
  13. Fuzzy Modeling for Control R. Babuska
  14. IEEE Trans. on Evolutionary Computation v.3 no.1 System design by constraint adaptation and differential evolution R. Stom https://doi.org/10.1109/4235.752918
  15. IEEE Trans. on Systems, Man and Cybernetics v.29 no.1 A fuzzy classifier with ellipsoidal regions for diagnosis problems S. Abe;R. Thawonmas;M. Kayama https://doi.org/10.1109/5326.740676
  16. Proc. of the Second International Conference on Intelligent Data Analysis A genetic approach for fuzzy clustering with a validity measure function S. Nascimiento;F. Moura-Pires
  17. See5/C5.0
  18. Proc. of the Second IEEE Conference on Evolutionary Computation v.2 Evolutionary fuzzy clustering T. Van Le https://doi.org/10.1109/ICEC.1995.487480
  19. UCI Repository of Machine Learning Databases