Abstract
We propose two methods for tuning membership functions of a fuzzy classifier by the support-vector-machine (SVM) like training. For each class, we define a membership function in the feature space. In the first method, we tune the slopes of the membership functions so that the margin between classes is maximized. This method is similar to a linear all-at-once SVM. We call this AAO tuning. In the second method, for each class the membership function is tuned so that the margin between the class and the remaining classes are maximized. This method is similar to a linear one-against-all SVM. This is called OAA tuning. According to the computer experiment, the kernel-discriminant-analysis (KDA) based fuzzy classifiers tuned by AAO tuning and by OAA tuning and SVM show comparable classification performance.
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References
Kuncheva, L.I.: Fuzzy Classifier Design. Physica-Verlag (2000)
Fullér, R.: Introduction to Neuro-Fuzzy Systems. Physica-Verlag (2000)
Abe, S.: Pattern Classification: Neuro-Fuzzy Methods and Their Comparison. Springer, Heidelberg (2001)
Lin, C.-T., Juang, C.-F.: An adaptive neural fuzzy filter and its applications. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 27(4), 635–656 (1997)
Kim, J., Kasabov, N.: HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks 12(9), 1301–1319 (1999)
Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 29(5), 601–618 (1999)
Abe, S., Thawonmas, R.: A fuzzy classifier with ellipsoidal regions. IEEE Trans. Fuzzy Systems 5(3), 358–368 (1997)
Kaieda, K., Abe, S.: KPCA-based training of a kernel fuzzy classifier with ellipsoidal regions. International Journal of Approximate Reasoning 37(3), 145–253 (2004)
Hosokawa, R., Abe, S.: Fuzzy classifiers based on kernel discriminant analysis. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 180–189. Springer, Heidelberg (2007)
Abe, S.: Support Vector Machines for Pattern Classification. Springer, Heidelberg (2005)
Torii, Y., Abe, S.: Decomposition techniques for training linear programming support vector machines. Neurocomputing (in press)
Intelligent Data Analysis Group, http://ida.first.fraunhofer.de/projects/bench/benchmarks.htm
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Morikawa, K., Abe, S. (2009). Improved Parameter Tuning Algorithms for Fuzzy Classifiers. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_114
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DOI: https://doi.org/10.1007/978-3-642-02490-0_114
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