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Case Retrieval Using Nonlinear Feature-Space Transformation

  • Rong Pan
  • Qiang Yang
  • Lei Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3155)

Abstract

Good similarity functions are at the heart of effective case-based reasoning. However, the similarity functions that have been designed so far have been mostly linear, weighted-sum in nature. In this paper, we explore how to handle case retrieval when the case base is nonlinear in similarity measurement, in which situation the linear similarity functions will result in the wrong solutions. Our approach is to first transform the case base into a feature space using kernel computation. We perform correlation analysis with maximum correlation criterion(MCC) in the feature space to find the most important features through which we construct a feature-space case base. We then solve the new case in the feature space using the traditional similarity-based retrieval. We show that for nonlinear case bases, our method results in a performance gain by a large margin. We show the theoretical foundation and empirical evaluation to support our observations.

Keywords

Similarity Case Base Transformation Nonlinear Case Bases 

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References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–52 (1994)Google Scholar
  2. 2.
    Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  3. 3.
    Hammond, K.: Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, San Diego (1989)Google Scholar
  4. 4.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)zbMATHGoogle Scholar
  5. 5.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  6. 6.
    Leake, D., Kinley, A., Wilson, D.: Case-based similarity assessment: Estimating adaptability from experience. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI Press, Menlo Park (1997)Google Scholar
  7. 7.
    Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.-R.: Kernel fisher discriminant analysis. In: Neural Networks for Signal Processing 9 – Proceedings of the 1999 IEEE Workshop, New York, IEEE, Los Alamitos (1999)Google Scholar
  8. 8.
    Roth, V., Steinhage, V.: Nonlinear discriminant analysis using kernel functions. In: Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 568–574. MIT Press, Cambridge (1999)Google Scholar
  9. 9.
    Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  10. 10.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  11. 11.
    Smola, A., Mangasarian, O., Schölkopf, B.: Sparse kernel feature analysis (1999)Google Scholar
  12. 12.
    Smyth, B., Keane, M.: Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, San Francisco, August 1995, pp. 377–382. Morgan Kaufmann, San Francisco (1995)Google Scholar
  13. 13.
    Smyth, B., Keane, M.: Adaptation-guided retrieval: Questioning the similarity assumption in reasoning. Artificial Intelligence 102(2), 249–293 (1998)zbMATHCrossRefGoogle Scholar
  14. 14.
    Smyth, B., McKenna, E.: Footprint-based retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 343–357. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  15. 15.
    Watson, I.: Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Mateo (1997)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rong Pan
    • 1
  • Qiang Yang
    • 2
  • Lei Li
    • 1
  1. 1.Software Engineering InstituteZhngshan UniversityGuangzhouChina
  2. 2.Department of Computer ScienceHong Kong University of Science and TechnologyKowloon, Hong KongChina

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