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Learning in Rough-Neuro-Fuzzy System for Data with Missing Values

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Parallel Processing and Applied Mathematics (PPAM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7203))

Abstract

Rough-neuro-fuzzy systems offer suitable way for classifying data with missing values. The paper presents a new implementation of gradient learning in the case of missing input data which has been adapted for rough-neuro-fuzzy classifiers. We consider the system with singleton fuzzification, Mamdani-type reasoning and center average defuzzification. Several experiments based on common benchmarks illustrating the performance of trained systems are shown. The learning and testing of the systems has been performed with various number of missing values.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Bartosz A. Nowak & Robert K. Nowicki

Authors
  1. Bartosz A. Nowak
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  2. Robert K. Nowicki
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Editor information

Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Dabrowskiego 69, 42-201, Czestochowa, Poland

    Roman Wyrzykowski  & Konrad Karczewski  & 

  2. Electrical Engineering and Computer Science Department, University of Tennessee, 1122 Volunteer Blvd, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

  3. Department of Informatics and Mathematical Modeling, Technical University of Denmark, Richard Petersens Plads, Building 321, 2800, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Nowak, B.A., Nowicki, R.K. (2012). Learning in Rough-Neuro-Fuzzy System for Data with Missing Values. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31464-3_51

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  • DOI: https://doi.org/10.1007/978-3-642-31464-3_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31463-6

  • Online ISBN: 978-3-642-31464-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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