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Case Based Systems: A Neuro-Fuzzy Method for Selecting Cases

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Soft Computing in Case Based Reasoning

Abstract

Various case based systems designed in soft computing paradigms are, first of all, described in brief. This is followed by a detailed description of a fuzzy neural network for selection of cases. The notion of fuzzy similarity is used for selecting the same from both overlapping and non-overlapping regions. The architecture of the network is adaptively determined through growing and pruning of hidden nodes under supervised training. The effectiveness of the cases, thus selected by the network, is demonstrated for pattern classification problem using the 1-NN rule with the cases as the prototypes. Results are presented using real-life data.

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Authors
  1. Rajat K. De
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  2. Sankar K. Pal
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Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Calcutta, 700 035, India

    Sankar K. Pal MTech, PhD, DIC, Fellow IEEE (Distinguished Scientist and Head) (Distinguished Scientist and Head)

  2. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Melbourne, Victoria, 3083, Australia

    Tharam S. Dillon BE, PhD, FSaRS, FIE, SMIEEE

  3. Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

    Daniel S. Yeung BA, MA, MSc, MBA, PhD, FHKIE

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© 2001 Springer-Verlag London Limited

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De, R.K., Pal, S.K. (2001). Case Based Systems: A Neuro-Fuzzy Method for Selecting Cases. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds) Soft Computing in Case Based Reasoning. Springer, London. https://doi.org/10.1007/978-1-4471-0687-6_10

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  • DOI: https://doi.org/10.1007/978-1-4471-0687-6_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-262-4

  • Online ISBN: 978-1-4471-0687-6

  • eBook Packages: Springer Book Archive

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