Architectures for evolving fuzzy rule-based classifiers

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2007, Pages 2050-2055

  Angelov, Plamen ^a   Zhou, Xiaowei ^a   Filev, Dimitar ^b   Lughofer, Edwin ^c  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b FORD MOTOR COMPANY   (United States)

^c JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

Author keywords

Evolving fuzzy rule based classifiers; Incremental learning from scratch; Mountain and subtractive clustering; Weighted recursive least squares

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; FUZZY RULES; MATHEMATICAL MODELS; PARAMETER ESTIMATION; USER INTERFACES;

EVOLVING FUZZY RULE-BASED CLASSIFIERS; INCREMENTAL LEARNING FROM SCRATCH; MOUNTAIN AND SUBTRACTIVE CLUSTERING; WEIGHTED RECURSIVE LEAST SQUARES;

COMPUTER ARCHITECTURE;

EID: 40949155135     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2007.4413728     Document Type: Conference Paper

References (39)

1. R. O. Duda, P.E. Hart, and D.G. Stork. Pattern Classificution - Second Edition. Wiley-Interscience, Chichester, West Sussex, England, 2000.
2. G. Fayyad, Piatetsky-Shapiro, P.-Smyth. From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, MIT Press, 1996.
3. P. Domingos and G. Hulten. Catching Up with the Data: Research Issues in Mining Data Streams, Workshop on Research Issues in Data Mining and Knowledge Discovery, Santa Barbara, CA, 2001.
4. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. Heidelberg, Germany: Springer Verlag, 2001.
5. V. N. Vapnik, The Statistical Learning Theory, Springer, 1998.
6. M. Butz, Rule-bused Evolutionary Online Learning Systems: A Principal Approach to LCS Analysis and Design, Physica Verlag, V.191, Berlin, Heidelberg, Germany, 2006, ISBN 3-540-25379-3.
7. J. A. Roubos, M. Setnes and J. Abonyi, Learning Fuzzy Classification Rules from Data, Information Sciences-Infomuxtics and Computer Science: An International Journal, vol. 150, pp. 77-93, 2003
8. C. M., Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.
9. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995
10. P. Angelov, Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Berlin, Germany: Springer Verlag, 2002.
11. P. Angelov, D. Filev, "An approach to on-line identification of evolving Takagi-Sugeno models", IEEE Trans. on Systems, Man and Cybernetics, part B, vol.34, No1, pp. 484-498, 2004.
12. P. Angelov, R. Buswell, Identification of Evolving Rule-based Models (2002) IEEE Transactions on Fuzzy Systems, vol. 10, No5, pp.667-677.
13. th Annual Conf. of the North American Fuzzy Information Processing Society, NAFIPS, 10-12 June 1999, New York, USA, pp.198-202.
14. P. Angelov, X. Zhou, F. Klawonn, Evolving Fuzzy Rule-based Classifiers, First 2007 IEEE International Conference on Computational Intelligence Applications for Signal and Image Processing, April 1-5, 2007, Honolulu, Hawaii, USA, to appear.
15. G. C. Goodwin, K. S. Sin, Adaptive Filtering, Prediction, and Control, Prentice Hall, ISBN-10: 013004069X, 1984.
16. F. Klawonn, P. Angelov, Evolving Extended Naive Bayes Classifier, In: S. Tsumoto, C.W. Clifton, N. Zhong, X. Wu, J. Liu, B.W. Wah, Y.-M. Cheung: Proc. Sixth IEEE Internatlonal Conference on Data Mining. IEEE, Los Alamitos (2006), pp. 643-647 (ISBN 0769527027).
17. P. Angelov, V. Giglio, C. Guardiola, E. Lughofer, An Approach to Model-based Fault Detection in In- dustrial Measurement Systems with Application to Engine Test Ben- ches, Measurement Science & Technology, v. 17 (7), 2006, 1809-1818.
18. P. Angelov, X. Zhou, Evolving Fuzzy Systems from Data Streams in Real-Time, Proc. 2006 International Symposium on Evolving Fuzzy Systems, UK, IEEE Press, pp.29-35, ISBN 0-7803-9719-3.
19. L.-X. Wang "Fuzzy Systems are Universal Approximators", Proc. FUZZ-IEEE, San Diego, CA, USA, pp.1163-1170, 1992.
20. P. Angelov, A Fuzzy Controller with Evolving Structure, Information Sciences, ISSN 0020-0255, vol.161, 2004, pp.21-35.
21. R. R. Yager, D.P. Filev, "Learning of Fuzzy Rules by Mountain Clustering," Proc. of SPIE Conf. on Application of Fuzzy Logic Technology, Boston, MA, USA, pp.246-254,1993.
22. st IEEE Symp. on Comp, Intelligence for Security and Defense Applic. (CISDA) 2007, April 1-5, 2007 Honolulu, HI, USA, to appear.
23. E. Lughofer, P. Angelov, X. Zhou, Evolving Single- and Multi-Model Fuzzy Classifiers with FLEXFIS-Class, IEEE Intern. Conf. on Fuzzy Syst., FUZZ-IEEE2007 23-26 July, 2007, London, England, to appear.
24. S. Pang, S. Ozawa, N. Kasabov, "Incremental Linear Discriminant Analysis for Classification of Data Streams ", IEEE Trans. on Systems, Man and Cybernetics, part B, vol.35, No5, pp. 905-914, 2005.
25. UCI Machine Learning Repository, http://www.ics.uci.edu/mlearn/ M~LRepository.html, accessed on 27 Jan 2007.
26. nd International Conference on Machine Learning, Bonn, Germany, pp.361-368, 2005.
27. E. Lughofer and E. P. Klement, "FLEXFlS: A variant for incremental learning of Takagi-Sugeno fuzzy systems, in Proc. FUZZ-IEEE 2005, Reno, Nevada, USA, 2005, pp.915-920.
28. R. Yager and D. Filev, Approximate Clustering via the Mountain Method, IEEE Trans. on Systems and Cybernetics, vol. 24 (8), 1994
29. S. Chiu, Fuzzy Model Identification based on Cluster Estimation, Journal of Intelligent and Fuzzy Systems, vol.2 (3), pp. 267-278, 1994
30. P. Angelov, C. Xydeas, D. Filev, On-line Identification of MIMO Evolving Takagi-Sugeno Fuzzy Models, Intern. Joint Conf. on Neural Networks and Intern. Conf. on Fuzzy Systems, UCNN-FUZZ-IEEE, Budapest, Hungary, 25-29 July, 2004, 55-60, ISBN 0-7803-8354-0.
31. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc, U.S.A., 1993.
32. L. Breiman, J. Friedman, C.J. Stone and R.A. Olshen, Classification and Regression Trees, Chapman and Hall, Boca Raton, 1993.
33. T. Hastie and R. Tibshirani, 1996. Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI). Vol. 18 (6), pp. 607-616, 1996.
34. M. Garofalakis, J.Gehrke and R. Rastogi, Querying and Mining Data Streams: you only get one look, Proc. of the 2002 ACM SIGMOD Intern. Conf. on Management of Data, pp. 635-641, ACM Press, 2002.
35. L.X. Wang, Fuzzy Systems are Universal Approximators, Proc. 1st IEEE Conf. Fuzzy Systems, pp. 1163-1169, San Diego, 1992.
36. E. Lughofer and U. Bodenhofer, Incremental Learning of Fuzzy Basis Function Networks with a Modified Version of Vector Quantization, Proc. of IPMU 2006, Paris, France, vol. 1, pp 56-63, 2006.
37. P. Angelov and E.Lughofer, Data-Driven Evolving Fuzzy Systems using eTS and FLEXFIS: Comparative Analysis, Intern. Journal of General Systems, to appear
38. T. Takagi and M. Sugeno, Fuzzy Identification of Systems and its Applications to Modeling and Control, IEEE Trans. on Systems, Man and Cybernetics, vol. 15(1), pp. 116-132, 1985.
39. th IFSA World Congress and 20th NAFIPS Intern. Conf.,25-28 July 2001, Vol. 3, pp. 1253-1258, Vancouver, BC, Canada, ISBN: 0-7803-7078-3.