A proposal on reasoning methods in fuzzy rule-based classification systems

International Journal of Approximate Reasoning

Volumn 20, Issue 1, 1999, Pages 21-45

  Cordón, Oscar ^a   Del Jesus, María José ^b   Herrera, Francisco ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

Author keywords

Fuzzy reasoning; Fuzzy rule based classification systems; Genetic algorithms

Indexed keywords

GENETIC ALGORITHMS; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; PATTERN RECOGNITION; PERFORMANCE;

FUZZY REASONING METHOD; FUZZY RULE BASED CLASSIFICATION SYSTEMS;

FUZZY SETS;

EID: 0032655554     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0888-613X(00)88942-2     Document Type: Article

References (30)

1. S. Abe, R. Thawonmas, A fuzzy classifier with ellipsoidal regions, IEEE Transactions on Fuzzy Systems 5 (3) (1997) 358-368.
2. C. Alsina, E. Trillas, L. Valverde, On some logical connectives for fuzzy set theory, Journal of Mathematical Analysis and Applications 93 (1983) 149-163.
3. J.E. Baker, Reducing bias and inefficiency in the selection algorithms, in: Proceedings of the Second International Conference on Genetic Algorithms, ICGA'87, Hillsdale, 1987, pp. 14-21.
4. A. Bárdossy, L. Duckstein, Fuzzy rule-based modeling with applications to geophysical, biological and engineering systems, CRC Press, Boca Raton, 1995.
5. J.C. Bezdek, S.K. Pal (Eds.), Fuzzy Models for Pattern Recognition, Methods that Search for Structures in Data, IEEE Press, New York, 1992.
6. Z. Chi, J. Wu, H. Yan, Handwritten numeral recognition using self-organizing maps and fuzzy rules, Pattern Recognition 28 (1) (1995) 59-66.
7. Z. Chi, H. Yan, T. Pham, Fuzzy algorithms with applications to image processing and pattern recognition, World Scientific, Singapore, 1996.
8. Z. Chi, H. Yan, ID3-Derived fuzzy rules and optimized defuzzification for handwritten numeral recognition, IEEE Transactions on Fuzzy Systems 4 (1) (1996) 24-31.
9. O. Cordón, F. Herrera, A. Peregrin, Applicability of the fuzzy operators in the design of fuzzy logic controllers, Fuzzy Sets and Systems 86 (1997) 15-41.
10. O. Cordón, M.J. del Jesus, F. Herrera, E, López, Selecting fuzzy rule-based classification systems with specific reasoning methods using genetic algorithms, in: Proceedings of the Seventh International Fuzzy Systems Association World Congress (IFSA'97), Prague, 1997, pp. 424-429.
11. D. Dubois, H. Prade, A review of fuzzy set aggregation connectives, Information Sciences 36 (1985) 85-121.
12. H. Dyckhoff, W. Pedrycz, Generalized means as model of compensative connectives. Fuzzy Sets and Systems 14 (1984) 143-154.
13. L. Fu, Rule generation from neural networks, IEEE Transactions on Systems, Man and Cybernetics 24 (8) (1994) 1114-1124.
14. D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, MA, 1989.
15. A. González, R. Pérez, Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets and Systems 96 (1998) 37-51.
16. F. Herrera, M. Lozano, J.L. Verdegay, Dynamic and heuristic fuzzy connectives-based crossover operators for controlling the diversity and convergence of real-coded genetic algorithms, International Journal of Intelligent Systems 11 (1996) 1013-1040.
17. F. Herrera, M. Lozano, J.L. Verdegay, Tackling real-coded genetic algorithms: Operators and tools for the behavioural analysis. Artificial Intelligence Review 12 (4) (1998) 265-319.
18. H. Ishibuchi, K. Nozaki, H. Tanaka, Distributed representation of fuzzy rules and its application to pattern classification, Fuzzy Sets and Systems 52 (1992) 21-32.
19. H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka, Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms, Fuzzy Sets and Systems 65 (1994) 237-253.
20. H. Ishibuchi, T. Morisawa, T. Nakashima, Voting schemes for fuzzy-rule-based classification systems, in: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'96), 1996, pp. 614-620.
21. L.I. Kuncheva, On the equivalence between fuzzy and statistical classifiers, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 4 (3) (1996) 245-253.
22. D.P. Mandal, C.A. Murthy, S.K. Pal, Formulation of a multivalued recognition system, IEEE Transactions on Systems, Man, and Cybernetics 22 (4) (1992) 607-620.
23. Z. Michalewicz, Genetic algorithms + data structures = evolutions programs, Springer, Berlin, 1992.
24. S. Pal, D.P. Mandai, Linguistic recognition system based on approximate reasoning, Information Sciences 61 (1992) 135-161.
25. K.S. Ray, J. Ghoshal, Approximate reasoning approach to pattern recognition, Fuzzy Sets and Systems 77 (1996) 125-150.
26. V. Uebele, S. Abe, M.-S. Lan, A neural-network-based fuzzy classifier, IEEE Transactions on Systems, Man, and Cybernetics 25 (2) (1995) 353-361.
27. L.-X. Wang, J.M. Mendel, Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man, and Cybernetics 22 (6) (1992) 1414-1427.
28. S.M. Weiss, C.A. Kulikowski, Computer systems that learn, Morgan Kaufmann, Los Altos, CA, 1991.
29. R.R. Yager, D.P. Filev, Parameterized and-like and or-like OWA operators, International Journal General Systems 22 (1994) 297-316.
30. L.A. Zadeh, Fuzzy sets and their applications to classification and clustering, in: J. van Ryzin (Ed.), Classification and Clustering, Academic Press, New York, 1977, pp. 251-299.