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)


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.


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.


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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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