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Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3614))

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Abstract

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. This paper presents selected membership functions extracted by a fuzzy neural network named NEWFM. The selected membership functions can capture the concentrated and essential information without sacrificing the classification capability. To verify the performance of the NEWFM, the well-known data set of Wisconsin breast cancer is performed. We applied NEWFM model to extract fuzzy membership functions for the UCI antibody deficiency syndrome diagnosis. Then selected features obtained by non-overlapped area measurement method are presented.

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Author information

Authors and Affiliations

  1. College of Software, Kyungwon University, Sungnam, 461-701, Korea

    Joon S. Lim

  2. Department of Computer Science, California State Univ., Fullerton, CA, 92834, USA

    Tae W. Ryu

  3. School of CSEE, Handong University, Pohang, 791-940, Korea

    Ho J. Kim

  4. Department of Medicine, University of California, Irvine, CA, 92868, USA

    Sudhir Gupta

Authors
  1. Joon S. Lim
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  2. Tae W. Ryu
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  3. Ho J. Kim
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  4. Sudhir Gupta
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Lim, J.S., Ryu, T.W., Kim, H.J., Gupta, S. (2005). Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_101

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  • DOI: https://doi.org/10.1007/11540007_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28331-7

  • Online ISBN: 978-3-540-31828-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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