TY - GEN A1 - Simiński, Krzysztof A2 - Korbicz, Józef - red. A2 - Uciński, Dariusz - red. PB - Zielona Góra: Uniwersytet Zielonogórski N2 - 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. L1 - http://www.zbc.uz.zgora.pl/Content/46995/AMCS_2012_22_2_18.pdf L2 - http://www.zbc.uz.zgora.pl/Content/46995 KW - neuro-fuzzy KW - ANNBFIS KW - missing values KW - marginalisation KW - imputation KW - rough fuzzy set KW - clustering T1 - Neuro-rough-fuzzy approach for regression modelling from missing data UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=46995 ER -