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

• Autorzy: Krzysztof Simiński
• 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.

Słowa kluczowe

EN

neuro-fuzzy; ANNBFIS; missing values; marginalisation; imputation; rough fuzzy set; clustering

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2012
• Tom: 22
• Numer: 2
• Strony: 461-476

Daty

wydano

2012

otrzymano

2011-02-01

poprawiono

2011-07-11

poprawiono

2011-10-25

Twórcy

autor

Krzysztof Simiński

• Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

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Identyfikatory

DOI

10.2478/v10006-012-0035-4