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Imputation of Missing Values by Inversion of Fuzzy Neuro-System

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Man–Machine Interactions 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

Abstract

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.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Krzysztof Siminski

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  1. Krzysztof Siminski
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Correspondence to Krzysztof Siminski .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

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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. https://doi.org/10.1007/978-3-319-23437-3_49

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

  • eBook Packages: EngineeringEngineering (R0)

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