A Methodology for Analyzing Case Retrieval from a Clustered Case Memory

  • Albert Fornells
  • Elisabet Golobardes
  • Josep Maria Martorell
  • Josep Maria Garrell
  • Núria Macià
  • Ester Bernadó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4626)

Abstract

Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.

Keywords

Case Retrieval Case Memory Organization Soft Case- Based Reasoning Complexity Measures Self-Organization Maps 

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References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundations issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
  2. 2.
    Basu, M., Ho, T.K.: Data Complexity in Pattern Recognition. In: Advanced Information and Knowledge Processing, Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Bernadó, E., Ho, T.K.: Domain of competence of XCS classifier system in complexity measurement space. IEEE Transaction Evolutionary Computation 9(1), 82–104 (2005)CrossRefGoogle Scholar
  4. 4.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  5. 5.
    Brown, M.: A Memory Model for Case Retrieval by Activation Passing. PhD thesis, University of Manchester (1994)Google Scholar
  6. 6.
    Chang, P., Lai, C.: A hybrid system combining self-organizing maps with case-based reasoning in wholesaler’s new-release book forecasting. Expert Syst. Appl. 29(1), 183–192 (2005)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetGoogle Scholar
  8. 8.
    Fornells, A., Golobardes, E., Martorell, J.M., Garrell, J.M., Bernadó, E., Macià, N.: Measuring the applicability of self-organization maps in a case-based reasoning system. In: 3rd Iberian Conference on Pattern Recognition and Image Analysis. LNCS, vol. 4478, pp. 532–539. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Fornells, A., Golobardes, E., Vernet, D., Corral, G.: Unsupervised case memory organization: Analysing computational time and soft computing capabilities. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 241–255. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Fornells, A., Golobardes, E., Vilasís, X., Martí, J.: Integration of strategies based on relevance feedback into a tool for retrieval of mammographic images. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 116–124. Springer, Heidelberg (2006) (Selected to be published in the International Journal of Neural Systems) CrossRefGoogle Scholar
  11. 11.
    Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(3), 289–300 (2002)CrossRefGoogle Scholar
  12. 12.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer Series in Information Sciences, vol. 8. Springer, Heidelberg (1984)MATHGoogle Scholar
  13. 13.
    Lenz, M., Burkhard, H.D., Brückner, S.: Applying case retrieval nets to diagnostic tasks in technical domains. In: Smith, I., Faltings, B.V. (eds.) Advances in Case-Based Reasoning. LNCS, vol. 1168, pp. 219–233. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  14. 14.
    Myllymaki, P., Tirri, H.: Massively parallel case-based reasoning with probabilistic similarity metrics (1993)Google Scholar
  15. 15.
    Nicholson, R., Bridge, D., Wilson, N.: Decision diagrams: Fast and flexible support for case retrieval and recommendation. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 136–150. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Pelleg, D., Moore, A.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference of Machine Learning, pp. 727–734. Morgan Kaufmann, San Francisco (2000)Google Scholar
  17. 17.
    Plaza, E., McGinty, L.: Distributed case-based reasoning. The Knowledge engineering review 20(3), 261–265 (2006)CrossRefGoogle Scholar
  18. 18.
    Rissland, E.L., Skalak, D.B., Friedman, M.: Case retrieval through multiple indexing and heuristic search. In: International Joint Conferences on Artificial Intelligence, pp. 902–908 (1993)Google Scholar
  19. 19.
    Schaaf, J.W.: Fish and Sink - an anytime-algorithm to retrieve adequate cases. In: Aamodt, A., Veloso, M.M. (eds.) Case-Based Reasoning Research and Development. LNCS, vol. 1010, pp. 538–547. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  20. 20.
    Vernet, D., Golobardes, E.: An unsupervised learning approach for case-based classifier systems. Expert Update. The Specialist Group on Artificial Intelligence 6(2), 37–42 (2003)Google Scholar
  21. 21.
    Wess, S., Althoff, K.D., Derwand, G.: Using k-d trees to improve the retrieval step in case-based reasoning. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) Topics in Case-Based Reasoning. LNCS, vol. 837, pp. 167–181. Springer, Heidelberg (1994)Google Scholar
  22. 22.
    Yang, Q., Wu, J.: Enhancing the effectiveness of interactive cas-based reasoning with clustering and decision forests. Applied Intelligence 14(1) (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Albert Fornells
    • 1
  • Elisabet Golobardes
    • 1
  • Josep Maria Martorell
    • 1
  • Josep Maria Garrell
    • 1
  • Núria Macià
    • 1
  • Ester Bernadó
    • 1
  1. 1.Grup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022 BarcelonaSpain

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