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Journal of Information Science and Engineering, Vol. 23 No. 3, pp. 839-853 (May 2007)

Multiple Compensatory Neural Fuzzy Networks Fusion Using Fuzzy Integral

Chi-Yung Lee and Cheng-Jian Lin*
Department of Computer Science and Information Engineering
Nan Kai Institute of Technology
Nantou, 542 Taiwan
*Department of Computer Science and Information Engineering
Chaoyang University of Technology
Taichung, 413 Taiwan
E-mail: cjlin@mail.cyut.edu.tw

This paper presents a novel method for combining multiple compensatory neural fuzzy networks (CNFN) using fuzzy integral. The fusion of multiple classifiers can overcome the limitations of a single classifier since the classifiers complement each other. A fuzzy integral is a better combination scheme than majority voting method that uses the subjectively defined relevance of classifiers. A combination of multiple CNFN classifiers with fuzzy integral (FI) is proposed to achieve data classification with higher accuracy than existing traditional methods. The advantage of the proposed method is that not only are the classification results combined but the relative importance of the different networks is also considered. Experimental results show that the fusion of multiple CNFNs using fuzzy integral can perform better than existing traditional methods.

Keywords: compensatory operation, neural fuzzy network, classification, fuzzy integral, fuzzy measures, on-line learning

Full Text () Retrieve PDF document (200705_11.pdf)

Received March 3, 2005; revised June 1 & September 16, 2005; accepted October 24, 2005.
Communicated by Chin-Teng Lin.