Abstract
More recently, neural network techniques and fuzzy logic inference systems have been receiving an increasing attention. At the same time, methods of establishing decision by a group of classifiers are regarded as a general problem in various application areas of pattern recognition. Fuzzy combiner proposed by authors, harnesses the support values from classifiers to provide final response having no other restrictions on their structure. The work on generalization of the two-class classification into multiclass classification by means of a fuzzy inference system is extended in this paper. Different methods of fuzzy combiner training are investigated and the result of computer experiments carried out on UCI benchmark datasets in the Matlab environment are presented.
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Wilk, T., Woźniak, M. (2012). Comparison of Fuzzy Combiner Training Methods. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_17
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DOI: https://doi.org/10.1007/978-3-642-34630-9_17
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