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2012 | 22 | 2 | 461-476
Tytuł artykułu

Neuro-rough-fuzzy approach for regression modelling from missing data

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.
Rocznik
Tom
22
Numer
2
Strony
461-476
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-02-01
poprawiono
2011-07-11
poprawiono
2011-10-25
Twórcy
  • Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
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  • Czogała, E. and Łęski, J. (2000). Fuzzy and Neuro-Fuzzy Intelligent Systems, Series in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg/New York, NY.
  • Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B 39(1): 1-38.
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Typ dokumentu
Bibliografia
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Identyfikator YADDA
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