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Prototype Extraction of a Single-Class Area for the Condensed 1-NN Rule

  • Marcin Raniszewski
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

The paper presents a new condensation algorithm based on the idea of a sample representativeness. For each sample in a dataset a representative measure is counted. Starting with samples with the highest value of the measure, each sample and all its voters (which constitute single-class area) are condensed in one averaged prototype-sample. The algorithm is tested on nine well-known datasets and compared with Jozwik’s condensation methods.

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References

  1. 1.
    Bezdek, J.C., Reichherzer, T.R., Lim, G.S., Attikiouzel, Y.: Multiple-prototype classifier design. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 28(1), 67–79 (1998)CrossRefGoogle Scholar
  2. 2.
    Chang, C.L.: Finding prototypes for nearest neighbor classifiers. IEEE Transactions on Computers C-23(11), 1179–1184 (1974)CrossRefGoogle Scholar
  3. 3.
    Chen, C.H., Jozwik, A.: A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recognition Letters 17(8), 819–823 (1996)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2001)zbMATHGoogle Scholar
  5. 5.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml Google Scholar
  6. 6.
    Jozwik, A., Kies, P.: Reference set size reduction for 1-NN rule based on finding mutually nearest and mutually furthest pairs of points. Computer Recognition Systems, Advances in Soft Computing 30, 195–202 (2005)CrossRefGoogle Scholar
  7. 7.
    Mollineda, R.A., Ferri, F.J., Vidal, E.: An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering. Pattern Recognition 35(12), 2771–2782 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Sanchez, J.S.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition 37(7), 1561–1564 (2004)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press - Elsevier, USA (2006)Google Scholar
  11. 11.
    Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38(3), 257–286 (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcin Raniszewski
    • 1
  1. 1.Computer Engineering DepartmentTechnical University of LodzLodzPoland

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