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Evolving single- and multi-model fuzzy classifiers with FLEXFIS-class

Lughofer, E. and Angelov, Plamen and Zhou, Xiaowei (2007) Evolving single- and multi-model fuzzy classifiers with FLEXFIS-class. In: Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International. IEEE, pp. 363-368. ISBN 1-4244-1209-9

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    Abstract

    [2] R. Santos, E. Dougherty, and J. A. Jaakko, “Creating fuzzy rules for image classification using biased data clustering,” in SPIE proceedings series (SPIE proc. ser.) International Society for Optical Engineering proceedings series. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 1999, pp. 151–159. [3] J. Q. S. Marin-Blazquez, “From approximative to descriptive fuzzy classifiers,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 4, pp. 484–497, 2002. [4] D. Nauck and R. Kruse, “Nefclass-x a soft computing tool to build readable fuzzy classifiers,” BT Technology Journal, vol. 16, no. 3, pp. 180–190, 1998. [5] J. Roubos, M. Setnes, and J. Abonyi, “Learning fuzzy classification rules from data,” Information SciencesInformatics and Computer Science: An International Journal, vol. 150, pp. 77–93, 2003. [6] D. Nauck, U. Nauck, and R. Kruse, “Generating classification rules with the neuro–fuzzy system NEFCLASS,” in Proc. Biennial Conference of the North American Fuzzy Information Processing Society NAFIPS’96, Berkeley, 1996. [7] N. S. H.-J. Rong, G.-B. Huang, and P. Saratchandran, “Sequential adaptive fuzzy inference system (safis) for nonlinear system identification and prediction,” Fuzzy Sets and Systems, vol. 157, no. 9, pp. 1260–1275, 2006. [8] N. K. Kasabov and Q. Song, “DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 144–154, 2002. [9] E. Lughofer and E. Klement, “FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems,” in Proceedings of FUZZIEEE 2005, Reno, Nevada, U.S.A., 2005, pp. 915–920. [10] C. Xydeas, P. Angelov, S. Chiao, and M. Reoullas, “Advances in eeg signals classification via dependant hmm models and evolving fuzzy classifiers,” International Journal on Computers in Biology and Medicine, special issue on Intelligent Technologies for Bio-Informatics and Medicine, vol. 36, no. 10, pp. 1064–1083, 2006. [11] P. Angelov, X. Zhou, and F. Klawonn, “Evolving fuzzy rule-based classifiers,” in 2007 IEEE International Conference on Computational Intelligence Application for Signal and Image Processing, Honolulu, Hawaii, USA, 2007, to appear. [12] E. Lughofer and U. Bodenhofer, “Incremental learning of fuzzy basis function networks with a modified version of vector quantization,” in Proceedings of IPMU 2006, volume 1, Paris, France, 2006, pp. 56–63. [13] R. Gray, “Vector quantization,” IEEE ASSP Magazine, pp. 4–29, 1984. [14] 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, no. 1, pp. 116–132, 1985. [15] L. Ljung, System Identification: Theory for the User. Upper Saddle River, New Jersey 07458: Prentice Hall PTR, Prentic Hall Inc., 1999. [16] P. Angelov and D. Filev, “An approach to online identification of Takagi- Sugeno fuzzy models,” IEEE Trans. on Systems, Man and Cybernetics, part B, vol. 34, no. 1, pp. 484–498, 2004. [17] A. Tsymbal, “The problem of concept drift: definitions and related work,” Department of Computer Science, Trinity College Dublin, Ireland, Tech. Rep. TCD-CS-2004-15, 2004. [18] L. Wang and J. Mendel, “Fuzzy basis functions, universal approximation and orthogonal least-squares learning,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 807–814, 1992. [19] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, Berlin, Heidelberg, Germany: Springer Verlag, 2001. [20] N. Draper and H. Smith, Applied Regression Analysis. Probability and Mathematical Statistics. New York: John Wiley & Sons, 1981. [21] P. Angelov and E. Lughofer, “Data-driven evolving fuzzy systems using ets and flexfis: Comparative analysis,” to appear in International Journal of General Systems, 2007. [22] E. Lughofer and E. Klement, “Online adaptation of Takagi-Sugeno fuzzy inference systems,” in Proceedings of CESA’2003—IMACS Multiconference, Lille, France, 2003, CD-Rom, paper S1-R-00-0175. [23] P. Angelov and D. Filev, “Simpl eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models,” in Proceedings of FUZZ-IEEE 2005, Reno, Nevada, U.S.A., 2005, pp. 1068–1073. [24] P. Angelov and X.-W. Zhou, “Evolving fuzzy systems from data streams in real-time,” in 2006 International Symposium on Evolving Fuzzy Systems, 2006, pp. 26–32. [25] L. Breiman, J. Friedman, C. Stone, and R. Olshen, Classification and Regression Trees. Boca Raton: Chapman and Hall, 1993. [26] P. Wasserman, Advanced Methods in Neural Computing. New York: Van Nostrand Reinhold, 1993.

    Item Type: Contribution in Book/Report/Proceedings
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    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 19217
    Deposited By: Dr. Plamen Angelov
    Deposited On: 30 Oct 2008 09:40
    Refereed?: No
    Published?: Published
    Last Modified: 03 Jun 2014 16:53
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/19217

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