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New Rough-Neuro-Fuzzy Approach for Regression Task in Incomplete Data

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

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Abstract

A fuzzy rule base is a crucial part of neuro-fuzzy systems. Data items presented to a neuro-fuzzy system activate rules in a rule base. For incomplete data the firing strength of the rules cannot be calculated. Some neuro-fuzzy systems impute the missing firing strength. This approach has been successfully applied. Unfortunately in some cases the imputed firing strength values are very low for all rules and data items are poorly recognized by the system. That may deteriorate the quality and reliability of elaborated results.

The paper presents a new method for handling missing values in neuro-fuzzy systems in a regression task. The new approach introduces a new imputation technique (imputation with group centres) to avoid very low firing strength for incomplete data items. It outperforms previous method (elaborates lower error rates), avoids numerical problems with very low firing strengths in all fuzzy rules of the system. The proposed systems elaborated interval answer without Karnik-Mendel algorithm. The paper is accompanied by numerical examples and statistical verification on real life data sets.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100, 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

    Stanisław Kozielski

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

    Dariusz Mrozek

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

    Paweł Kasprowski

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

    Bożena Małysiak-Mrozek

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

    Daniel Kostrzewa

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Siminski, K. (2016). New Rough-Neuro-Fuzzy Approach for Regression Task in Incomplete Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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