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A Particle Swarm Optimization Approach for the Case Retrieval Stage in CBR

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Research and Development in Intelligent Systems XXVII (SGAI 2010)

Abstract

Finding the good experiment to reuse from the case memory is the key of success in Case Based Reasoning (CBR). The paper presents a novel associative memory model to perform this task. The algorithm is founded on a Particle Swarm Optimization (PSO) approach to compute the neighborhood of a new problem. Then, direct access to the cases in the neighborhood is performed. The model was experimented on the Adult dataset, acquired from the University of California at Irvine Machine Learning Repository and compared to flat memory model for performance. The obtained results are very promising.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Quebec at Montreal, CP. 8888, Succ. Centre-ville, Montréal, QC, H3C3P8, Canada

    Nabila Nouaouria & Mounir Boukadoum

Authors
  1. Nabila Nouaouria
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  2. Mounir Boukadoum
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Corresponding authors

Correspondence to Nabila Nouaouria or Mounir Boukadoum .

Editor information

Editors and Affiliations

  1. Dept. Computer Science and, Software Engineering, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , Faculty of Technology, De Montford University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Adrian Hopgood

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© 2011 Springer-Verlag London Limited

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Nouaouria, N., Boukadoum, M. (2011). A Particle Swarm Optimization Approach for the Case Retrieval Stage in CBR. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_15

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  • DOI: https://doi.org/10.1007/978-0-85729-130-1_15

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-129-5

  • Online ISBN: 978-0-85729-130-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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