Imputation of Missing Values by Inversion of Fuzzy Neuro-System

Conference paper

DOI: 10.1007/978-3-319-23437-3_49

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 391)
Cite this paper as:
Siminski K. (2016) Imputation of Missing Values by Inversion of Fuzzy Neuro-System. In: Gruca A., Brachman A., Kozielski S., Czachórski T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham


Incomplete data are common and require special techniques. The essential techniques are: marginalisation, imputation, and rough sets. The paper presents the imputation by inversion of the neuro-fuzzy system. First the neuro-fuzzy systems is trained with complete data. Next the system is inverted and the missing values are imputed. The complete and imputed data are used to train the final neuro-fuzzy system. The technique is limited to data items with one missing value. The paper is accompanied by numerical examples and statistical verification.


Evolutionary optimisation Gradient descent Memetic algorithm Big-Bang-Big-crunch 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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