A neurofuzzy classifier for two class problemsGao, M., Hong, X. and Harris, C. J. (2012) A neurofuzzy classifier for two class problems. In: Proceedings of the UKCI 2012, the 12th UK Workshop on Computational Intelligence. Conference Publications. IEEE, pp. 1-6. ISBN 9781467343916 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/UKCI.2012.6335763 Abstract/SummaryA neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.
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