Improving the performance of fuzzy classification systems by membership function learning and feature selection

IEEE International Conference on Plasma Science

Volumn 1, Issue , 2002, Pages 488-493

  Nakashima, Tomoharu ^a   Nakai, Gaku ^a   Ishibuchi, Hisao ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE SELECTION; FUZZY CLASSIFICATION SYSTEMS;

COMPUTER SIMULATION; ERROR CORRECTION; FUZZY SETS; LEARNING SYSTEMS; MEMBERSHIP FUNCTIONS; PATTERN RECOGNITION;

KNOWLEDGE BASED SYSTEMS;

EID: 0036458246     PISSN: 07309244     EISSN: None     Source Type: Journal    
DOI: 10.1109/FUZZ.2002.1005039     Document Type: Article

References (12)

1. S. Abe and M.-S. Lan, "A method for fuzzy rules extraction directly from numerical data and its application to pattern classification", IEEE Trans. on Fuzzy Systems, Vol.3, No.1, pp.18-28, February, 1995.
2. F.C.-H. Rhee and R. Krishnapuram, "Fuzzy rule generation methods for high-level computer vision", Fuzzy Sets and Systems, Vol.60, No.3, pp.245-258, December, 1993.
3. H. Ishibuchi, K. Nozaki, and H. Tanaka, "Distributed representation of fuzzy rules and its application to pattern classification", Fuzzy Sets and Systems, Vol.52, No.1, pp.21-32, November, 1992.
4. R. Krishnapuram, and F.C.-H. Rhee, "Compact fuzzy rule base generation methods for computer vision", Proc. of 2nd IEEE International Conference on Fuzzy Systems, San Francisco, March 28-April 1, pp.809-814, 1993.
5. M. Skubic, S.P. Castrianni, and R.A. Volz, "Identifying contact formations from force signals: A comparison of fuzzy and neural network classifiers", Proc. of International Conference on Neural Networks, Houston, June 9-12, pp.1623-1628, 1998.
6. H. Ishibuchi, T. Nakashima, and T. Morisawa, "Simple fuzzy rule-based classification systems performed well on commonly used real-world data sets", Proc. of North American Fuzzy Information Processing Society Meeting, Syracuse, September 21-24, pp.251-256, 1997.
7. R.C. Holte, "Very simple classification rules perform well on most commonly used datasets", Machine Learning, Vol.11, pp.63-90, 1993.
8. T. Nakashima and H. Ishibuchi, "Supervised and unsupervised fuzzy discretization of continuous attributes for pattern classification problems", Proc. of Knowledge-Based Intelligent Information Engineering Systems & Applied Technologies, Osaka, pp.32-36.
9. J. Dougherty, R. Kohavi, and M. Sahami, "Supervised and unsupervised discretization of continuous features," Proc. of the 12th International Conference on Machine Learning, Tahoe City, CA, pp.194-202, 1995.
10. D. Nauck and R. Kruse, "A neuro-fuzzy method to learn fuzzy classification rules from data", Fuzzy Sets and Systems, Vol. 89, No.3, pp. 277-288, 1997.
11. J.R. Quinlan, C4.5: Programs for machine learning, Morgan Kaufmann Publishers, San Mateo, 1993.
12. L.-X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples", IEEE Trans. on Systems, Man and Cybernetics, Vol. 22, No. 6, pp. 1414-1427, 1992.