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Learning Similarity Measure of Nominal Features in CBR Classifiers

  • Yan Li
  • Simon Chi-Keung Shiu
  • Sankar Kumar Pal
  • James Nga-Kwok Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

Nominal feature is one type of symbolic features, whose feature values are completely unordered. The most often used existing similarity metrics for symbolic features is the Hamming metric, where similarity computation is coarse-grained and may affect the performance of case retrieval and then the classification accuracy. This paper presents a GA-based approach for learning similarity measure of nominal features for CBR classifiers. Based on the learned similarities, the classification accuracy can be improved, and the importance of each nominal feature can be analyzed to enhance the understanding of the used data sets.

Keywords

Similarity Measure Classification Accuracy Class Label Nominal Feature Information Granule 
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.

References

  1. 1.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  2. 2.
    Pal, S.K., Shiu, S.C.K.: Foundations of Soft Case-Based Reasoning. John Wiley, New York (2004)CrossRefGoogle Scholar
  3. 3.
    Aha, D.W., Dennis, K., Marc, K.A.: Instance-Based Learning Algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  4. 4.
    Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies 36, 267–287 (1992)CrossRefGoogle Scholar
  5. 5.
    Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Gowda, K.C., Diday, E.: Symbolic clustering using a new similarity measure. IEEE trans. Systems, man Cybernetics 22, 368–378 (1992)CrossRefGoogle Scholar
  7. 7.
    Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, Department of Information and Computer Science. University of California, Irvine, http://www.ics.uci.edu/~mlearn/MLRepository.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yan Li
    • 1
  • Simon Chi-Keung Shiu
    • 1
  • Sankar Kumar Pal
    • 2
  • James Nga-Kwok Liu
    • 1
  1. 1.Department of ComputingHong Kong Polytechnic UniversityKowloon, HongKong
  2. 2.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

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