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Fuzzy Rule Extraction Using Radial Basis Function Neural Networks in High-Dimensional Data

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Intelligent Knowledge-Based Systems

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Abstract

Rule learning is an increasingly important topic in both machine learning and data mining research. Machine learning concerns the development of algorithms or programs, which learn knowledge or skills while data mining is about the discovery of patterns or rules hidden in the data. Given a set of corresponding input-output values of a system, the challenge consists of identifying and formulating the relations between the input-output values in order to describe the system. To identify such relations, a functional input-output description may be provided. However, when dealing with complex processes, this is generally not feasible. One needs to look for alternative methods. The use of fuzzy models described through fuzzy rules has proven to be successful. Indeed, general knowledge about actions or conclusions can be expressed by a set of fuzzy If-Then rules of a Fuzzy Inference System (FIS).

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Author information

Authors and Affiliations

  1. Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands

    F. Admiraal-Behloul

  2. Division of Image Processing Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

    Johan H. C. Reiber

Authors
  1. F. Admiraal-Behloul
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  2. Johan H. C. Reiber
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Editors and Affiliations

  1. University of California, Los Angeles, USA

    Cornelius T. Leondes

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© 2005 Kluwer Academic Publishers

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Admiraal-Behloul, F., Reiber, J.H.C. (2005). Fuzzy Rule Extraction Using Radial Basis Function Neural Networks in High-Dimensional Data. In: Leondes, C.T. (eds) Intelligent Knowledge-Based Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4020-7829-3_44

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  • DOI: https://doi.org/10.1007/978-1-4020-7829-3_44

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7746-3

  • Online ISBN: 978-1-4020-7829-3

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