Evolutionary Generation of Prototypes for a Learning Vector Quantization Classifier

  • L. P. Cordella
  • C. De Stefano
  • F. Fontanella
  • A. Marcelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


An evolutionary computation based algorithm for data classification is presented. The proposed algorithm refers to the learning vector quantization paradigm and is able to evolve sets of points in the feature space in order to find the class prototypes. The more remarkable feature of the devised approach is its ability to discover the right number of prototypes needed to perform the classification task without requiring any a priori knowledge on the properties of the data analyzed. The effectiveness of the approach has been tested on satellite images and the obtained results have been compared with those obtained by using other classifiers.


Feature Space Recognition Rate Crossover Operator Near Neighbor Reference Vector 
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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  1. 1.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & sons, Inc., Chichester (2001)zbMATHGoogle Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  3. 3.
    Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 1999. LNCS (LNAI), vol. 1813. Springer, Heidelberg (2000)Google Scholar
  4. 4.
    Giordana, A., Neri, F.: Search-intensive concept induction. Evolutionary Computation 3, 375–416 (1995)CrossRefGoogle Scholar
  5. 5.
    Greene, D.P., Smith, S.F.: Competition-based induction of decision models from examples. Machine Learning, 229–257 (1993)Google Scholar
  6. 6.
    Janikow, C.Z.: A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 189–228 (1993)Google Scholar
  7. 7.
    De Jong, K.A., Spears, W.M., Gordon, D.F.C., Janikow, Z.: Using genetic algorithms for concept learning. Machine Learning, 161–188 (1993)Google Scholar
  8. 8.
    Agnelli, D., Bollini, A., Lombardi, L.: Image classification: an evolutionary approach. Pattern Recognition Letters 23, 303–309 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Rauss, P.J., Daida, J.M., Chaudhary, S.A.: Classification of spectral image using genetic programming. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 726–733 (2000)Google Scholar
  10. 10.
    Kishore, J.K., Patnaik, L.M., Mani, V., Agrawal, V.K.: Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation 4, 242–258 (2000)CrossRefGoogle Scholar
  11. 11.
    Mendes, R., Voznika, F., Freitas, A., Nievola, J.: Discovering fuzzy classification rules with genetic programming and co-evolution. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 314–325. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer-Verlag New York, Inc., Secaucus (2001)zbMATHGoogle Scholar
  13. 13.
    Karayiannis, N.B.: Learning vector quantization: A review. International Journal of Smart Engineering System Design 1, 33–58 (1997)Google Scholar
  14. 14.
    Muhlenbein, H., Schlierkamp-Voosen, D.: The science of breeding and its application to the breeder genetic algorithm (bga). Evolutionary Computation 1, 335–360 (1993)CrossRefGoogle Scholar
  15. 15.
    Blickle, T., Thiele, L.: A comparison of selection schemes used in genetic algorithms. Technical Report 11, Swiss Federal Institute of Technology (ETH), Gloriastrasse 35, 8092 Zurich, Switzerland (1995)Google Scholar
  16. 16.
    D’Elia, C., Poggi, G., Scarpa, G.: A tree-structured random markov field model for bayesian image segmentation. IEEE Transactions on Image Processing 12, 1259–1273 (2003)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Ahalt, S., Krishnamurthy, A., Chen, P., Melton, D.: Competitive learning algorithms for vector quantizationn. Neural Networks 3, 277–290 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • L. P. Cordella
    • 1
  • C. De Stefano
    • 2
  • F. Fontanella
    • 1
  • A. Marcelli
    • 3
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Napoli Federico IINapoliItaly
  2. 2.Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell’Informazione e Matematica IndustrialeUniversità di CassinoCassino (FR)Italy
  3. 3.Dipartimento di Ingegneria dell’Informazione e Ingegneria ElettricaUniversità di SalernoFisciano (SA)Italy

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