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

  • Nabila Nouaouria
  • Mounir Boukadoum
Conference paper

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.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Memory Model Case Base Reasoning Memory Organization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    S.K. Pal, S. C. Shiu, “Foundations of Soft Case-Based Reasoning”, ed. John Wiley & Sons Inc, (2004).Google Scholar
  2. 2.
    Kolodner J., “Case Based Reasoning”, ed. Morgan Kaufmann, (1993).Google Scholar
  3. 3.
    I. Bichindaritz, “Memory Organization As The Missing Link Between Case-Based Reasoning And Information Retrieval In Biomedicine”, Computational Intelligence, Volume 22, Number 3/4, pp: 148-160, Blackwell Publishing , (2006)Google Scholar
  4. 4.
    Bartsch-Spörl B. & al., “Case-Based Reasoning Surveys and Future Direction”, ed Springer, (1999).Google Scholar
  5. 5.
    Lenz M. et al, “Diagnosis and decision support”, in: LNAI 1400, ed. Springer, (1998).Google Scholar
  6. 6.
    Schaaf J. W., “Fish and Shrink. A next step towards efficient case retrieval in large scaled case bases”, in Advances in Case-Based Reasoning, pp: 362-376, Lecture Notes in Computer Science, Ed. Springer Berlin / Heidelberg, (1996).Google Scholar
  7. 7.
    Lenz M., « Case Retrieval Nets as a Model for Building Flexible Information Systems », PhD Dissertation, Humboldt University, Berlin, Germany, (1999).Google Scholar
  8. 8.
    Kennedy,J., Eberhart, R. Swarm Intelligence. Ed. Morgan Kaufmann, (2001).Google Scholar
  9. 9.
    A. Abraham, He Guo, Hongbo Liu, “Swarm Intelligence: Foundations, Perspectives and Applications”, Studies in Computational Intelligence (SCI) 26, 3–25, Springer-Verlag Berlin Heidelberg (2006).Google Scholar
  10. 10.
    Engelbrecht, A., P. Computational Intelligence An Introduction. John Willey & Sons Editions, (2007).Google Scholar
  11. 11.
    Clerc, M. L’optimisation par essaim particulaire: versions paramétriques et adaptatives. Ed. Hermes science publications, Lavoisier, Paris, (2005).Google Scholar
  12. 12.
    Chandramouli, K. and Izquierdo, E., "Image Classification using Chaotic Particle Swarm Optimization," in Proc. International Conference on Image Processing (ICIP '06), (2006).Google Scholar
  13. 13.
    Kennedy J, Eberhart R, Particle swarm optimization. In: Proceedings of the 4th IEEE international conference on neural networks, Perth, Australia, pp 1942–1948, (1995).Google Scholar
  14. 14.
    M. G. H. Omran, A. Engelbrecht, and A. Salman, "Barebones particle swarm for integer programming problems," in Proc. IEEE Swarm Intelligence Symposium, (2007).Google Scholar
  15. 15.
    Asuncion, A. & Newman, D.J.. UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science, (2007).
  16. 16.
    S. Boriah, V. Chandola, V. Kumar, “Similarity Measures for Categorical Data: A Comparative Evaluation”, in Proceedings of SIAM Data Mining Conference, Atlanta, GA, April (2008).Google Scholar
  17. 17.
    I. Bichindaritz, “Memory Structures and Organization in Case-Based Reasoning”, Studies in Computational Intelligence (SCI) 73, 175–194, Springer-Verlag Berlin Heidelberg (2008).Google Scholar
  18. 18.
    N. Nouaouria, M. Boukadoum, “Case Retrieval with Combined Adaptability and Similarity Criteria: Application to Case Retrieval Nets”, In: Proceedings of ICCBR’2010, I. Bichindaritz and S. Montani (Eds.), LNAI 6176, pp. 242–256, (2010).Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Quebec at MontrealMontréalCanada

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