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dc.contributor.authorShtovba, S. D.en
dc.contributor.authorPankevich, O. D.en
dc.contributor.authorNagorna, A. V.en
dc.contributor.authorШтовба, С. Д.uk
dc.contributor.authorПанкевич, О. Д.uk
dc.contributor.authorНагорна, А. В.uk
dc.date.accessioned2019-05-14T13:25:36Z
dc.date.available2019-05-14T13:25:36Z
dc.date.issued2015
dc.identifier.citationShtovba S. D. Analysis of criteria for fuzzy classifier learning [Text] / S. D. Shtovba, O. D. Pankevich, A. V. Galushchak // Automatic Control and Computer Sciences. – 2015. – Vol. 48, № 3. - P. 123-132.uk
dc.identifier.urihttp://ir.lib.vntu.edu.ua//handle/123456789/24763
dc.description.abstractIn a fuzzy classifier, the maping “inputs–output” is described by the linguistic 〈If–then〉 rules, the antecedents in which contain the fuzzy terms “low,” “average,” “high,” and so on. To increase the correctness, the fuzzy classifier is learned by using experimental data. The problems with equal and different costs of various classification errors are discussed. A new criterion is offered for problems with undistinguishable types of errors, in addition to the two known criteria. A new one implies that the distance between the desired and real fuzzy results of classification for the cases of a wrong decision is weighted by the penalty factor. The learning criteria are generalized for problems of classification with the cost matrix. The conducted computer experiments on the wine recognition and heart disease diagnostics problems show that the best quality parameters of tuning fuzzy classifiers are achieved by a new learning criterion.en
dc.language.isouk_UAuk_UA
dc.publisherInstitute of Electronics and Computer Sciencesen
dc.relation.ispartofAutomatic Control and Computer Sciences. №3 : 123-132.uk
dc.titleAnalysis of criteria for fuzzy classifier learninguk
dc.typeArticle


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