Abstract
This paper proposes a method of neuro-fuzzy for classification using adaptive dynamic clustering. The method has three parts, the first part is to find the proper number of membership functions by using adaptive dynamic clustering and transform to binary value in a second step. The final step is classification part using neural network. Furthermore the weights from the learning process of the neural network are used as feature eliminates to perform the rule extraction. The experiments used dataset form UCI to verify the proposed methodology. The result shows the high performance of the proposed method.
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References
Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, New York (2008)
Badiru, A.D., Cheung, J.Y.: Fuzzy Engineering Expert System with Neural Network Applications. John Wiley, New York (2002)
Kriesel, D.: A Brief Introduction to Neural Networks. Retrieved August, 15, 2011, pp. 37-124 (2007)
Chakraborty, D., Pal, N.R.,: A Neuro-Fuzzy Scheme for Simultaneous Feature Selection and Fuzzy Rule-Based Classification. In IEEE Transactions on Neural Network, Vol., 15, NO. 1, January 2004, pp 110-123, (2004).
Eiamkanitchat, N., Theera-Umpon, N., Auephanwiriyakul, S.: A Novel Neuro-Fuzzy Method for Linguistic Feature Selection and Rule-Based Classification. In: The 2nd International Conference on Computer and Automation Engineering (ICCAE), pp. 247-252. IEEE Press (2010).
Eiamkanitchat, N., Theera-Umpon, N., Auephanwiriyakul, S.: Colon Tumor Microarray Classification Using Neural Network with Feature Selection and Rule-Based Classification. In: Zeng, Z., Wang, J. (eds.) LNEE, vol. 67, pp. 363–372. Springer, Heidelberg (2010)
Wongchomphu, P. Eiamkanitchat, N.: Enhance Neuro-Fuzzy System for Classification Using Dynamic Clustering. In: The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), pp. 1-6. IEE Press (2014).
J. Kiefer.: Sequential minimax search for a maximum. Proceedings of the American Mathematical Society, vol. 4, pp. 502-506, (1953).
Zuo, Q., Yin, X., Zhou, J.,Kwak, BJ., Chung, K.: Implementation of Golden Section Search Method in SAGE Algorithm. In: Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP), pp 2028-2032, (2011).
Yeam, DH., Park, J.b., Joo, Y.H.: Selection of coefficient for Equalizer in Optical Disc Drive by Golden Section Search. IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, May (2010).
Bache, K, Lichman, M, (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
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Napook, P., Eiamkanitchat, N. (2015). The Adaptive Dynamic Clustering Neuro-Fuzzy System for Classification. In: Kim, K. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46578-3_85
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DOI: https://doi.org/10.1007/978-3-662-46578-3_85
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