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The Learning of Neuro-Fuzzy Classifier with Fuzzy Rough Sets for Imprecise Datasets

Type of Publication: In Book
Year: 2014
Authors:
Editor: Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada
Volume: 8467
Pages: 256–266
Publisher: Springer International Publishing
Series: Lecture Notes in Computer Science
ISBN: 978-3-319-07172-5
BibTex:
Abstract:
The paper concerns the architecture of a neuro-fuzzy classifier with fuzzy rough sets which has been developed to process imprecise data. A raw output of such system is an interval which has to be interpreted in terms of classification afterwards. To obtain a credible answer, the interval should be as narrow as possible; however, its width cannot be zero as long as input values are imprecise. In the paper, we discuss the determination of classifier parameters using the standard gradient learning technique. The effectiveness of the proposed method is confirmed by several simulation experiments. Learning of Neuro-Fuzzy Classifier with Fuzzy Rough Sets for Imprecise Datasets

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