Fuzzy data analysis with NEFCLASS

International Journal of Approximate Reasoning

Volumn 32, Issue 2-3, 2003, Pages 103-130

  Nauck, Detlef D ^a  

^a MARTLESHAM HEATH   (United Kingdom)

Author keywords

Fuzzy system; Intelligent data analysis; Neuro fuzzy methods; Rule learning

Indexed keywords

APPROXIMATION THEORY; DATA REDUCTION; FUNCTIONS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; TIME SERIES ANALYSIS;

NEURO-FUZZY SYSTEMS;

FUZZY SETS;

EID: 0037308121     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0888-613X(02)00079-8     Document Type: Article

References (30)

1. Azvine B., Azarmi N., Nauck D. Intelligent Systems and Soft Computing: Prospects, Tools and Applications. Lecture Notes in Artificial Intelligence. vol. 1804:2000;Springer, Berlin.
2. Bersini H., Bontempi G. Now comes the time to defuzzify neuro-fuzzy models. Fuzzy Sets and Systems. 90:1997;161-170.
3. H. Bersini, G. Bontempi, M. Birattari, Is readability compatible with accuracy? From neuro-fuzzy to lazy learning, in: Fuzzy-Neuro Systems'98 - Computational Intelligence, Proceedings of the Fifth International Workshop on Fuzzy-Neuro-Systems'98 (FNS'98) in Munich, Germany, vol. 7 of Proceedings in Artifical Intelligence, infix, Sankt Augustin, 1998, pp. 10-25.
4. Berthold M., Hand D.J. Intelligent Data Analysis: An Introduction. 1999;Springer, Berlin.
5. Casillas J., Cordon O., Herrera F., Magdalena L. Trade-off between Accuracy and Interpretability in Fuzzy Rule-Based Modelling. Studies in Fuzziness and Soft Computing. 2002;Physica-Verlag, Heidelberg.
6. Cordon O., Herrera F., Zwir I. Analyzing and extending hierarchical systems of linguistic rules. Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver. 2001;1121-1126.
7. Hand D.J. Intelligent data analysis: issues and opportunities. Int. J. Intelligent Data Analysis. 2(2):1998;. electronic journal http://www.elsevier.com/locate/ida.
8. Jang J.-S., Sun C., Mizutani E. Neuro Fuzzy and Soft Computing. 1997;Prentice Hall, Upper Saddle River, NJ.
9. Janikow C.Z. Fuzzy decision trees: issues and methods. IEEE Trans. Systems, Man Cybern. B. 28(1):1998;1-14.
10. Keller J.M., Tahani H. Backpropagation neural networks for fuzzy logic. Inf. Sci. 62:1992;205-221.
11. Kinzel J., Klawonn F., Kruse R. Modifications of genetic algorithms for designing and optimizing fuzzy controllers. Proceedings of the IEEE Conference on Evolutionary Computation. 1994;28-33 IEEE Press, Orlando, FL.
12. Klawonn F., Gebhardt J., Kruse R. Fuzzy control on the basis of equality relations with an example from idle speed control. IEEE Trans. Fuzzy Systems. 3(3):1995;336-350.
13. Kruse R., Gebhardt J., Klawonn F. Foundations of Fuzzy Systems. 1994;Wiley, Chichester.
14. Kruse R., Meyer K.D. Statistics with Vague Data. 1987;Reidel, Dordrecht.
15. Lee M., Takagi H. Integrating design stages of fuzzy systems using genetic algorithms. Proceedings of the IEEE International Conference on Fuzzy Systems, San Francisco. March 1993;612-617.
16. Mitra S., Kuncheva L. Improving classification performance using fuzzy mlp and two-level selective partitioning of the feature space. Fuzzy Sets and Systems. 70:1995;1-13.
17. Nauck D. Using symbolic data in neuro-fuzzy classification. Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society (NAFIPS'99). 1999;536-540 IEEE Press, New York, NY.
18. Nauck D. Adaptive rule weights in neuro-fuzzy systems. Neural Computing and Applications. 9(1):2000a;60-70.
19. D. Nauck, Data analysis with neuro-fuzzy methods. Habilitation Thesis, Otto-von-Guericke University of Magdeburg, Faculty of Computer Science, Magdeburg, Germany, 2000b. Available from 〈 http://www.neuro-fuzzy.de/̃nauck〉.
20. Nauck D., Klawonn F., Kruse R. Foundations of Neuro-Fuzzy Systems. 1997;Wiley, Chichester.
21. Nauck D., Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets and Systems. 89:1997;277-288.
22. Nauck D., Kruse R. NEFCLASS-X - a soft computing tool to build readable fuzzy classifiers. BT Technol. J. 16(3):1998;180-190.
23. Nauck D., Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intell. Med. 16(2):1999;149-169.
24. Nauck D., Kruse R. NEFCLASS-J - a java-based soft computing tool. Azvine B., Azarmi N., Nauck D. Intelligent Systems and Soft Computing: Prospects, Tools and Applications. Lecture Notes in Artificial Intelligence. vol. 1804:2000;143-164 Springer, Berlin.
25. Nauck D., Nauck U., Kruse R. NEFCLASS for JAVA - new learning algorithms. Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society (NAFIPS'99). 1999;472-476 IEEE Press, New York, NY.
26. Pal S.K., Mitra S. Multi-layer perceptron, fuzzy sets and classification. IEEE Trans. Neural Networks. 3:1992;683-697.
27. Wang L.-X., Mendel J.M. Generation rules by learning from examples. International Symposium on Intelligent Control. 1991;263-268 IEEE Press, New York.
28. Wolberg W., Mangasarian O. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. 87:1990;9193-9196.
29. Zadeh L.A. Fuzzy logic, neural networks and soft computing. Commun. ACM. 37(3):1994;77-84.
30. Zadeh L.A. Fuzzy logic and the calculi of fuzzy rules and fuzzy graphs: a precis. Int. J. Multiple-Valued Logic. 1:1996;1-38.