Analysis of criteria for fuzzy classifier learning
Репозитарій Вінницького Національного Технічного Університету
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Analysis of criteria for fuzzy classifier learning
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| Creator |
Shtovba, S. D.
Pankevich, O. D. Nagorna, A. V. Штовба, С. Д. Панкевич, О. Д. Нагорна, А. В. |
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| Description |
In 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.
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| Date |
2019-05-14T13:25:36Z
2019-05-14T13:25:36Z 2015 |
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| Type |
Article
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| Identifier |
Shtovba 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.
http://ir.lib.vntu.edu.ua//handle/123456789/24763 |
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| Language |
uk_UA
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| Relation |
Automatic Control and Computer Sciences. №3 : 123-132.
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| Format |
application/pdf
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| Publisher |
Institute of Electronics and Computer Sciences
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