A Modal Symbolic Classifier for Interval Data

  • Fabio C. D. Silva
  • Francisco de A.T. de Carvalho
  • Renata M. C. R. de Souza
  • Joyce Q. Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


A modal symbolic classifier for interval data is presented. The proposed method needs a previous pre-processing step to transform interval symbolic data into modal symbolic data. The presented classifier has then as input a set of vectors of weights. In the learning step, each group is also described by a vector of weight distributions obtained through a generalization tool. The allocation step uses the squared Euclidean distance to compare two modal descriptions. To show the usefulness of this method, examples with synthetic symbolic data sets are considered.


Modal Data Interval Data Symbolic Data Monte Carlo Experience Symbolic Variable 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Appice, A., D’Amato, C., Esposito, F., Malerba, D.: Classification of symbolic objects: A lazy learning approach. Intelligent Data Analysis. Special issue on Symbolic and Spatial Data Analysis: Mining Complex Data Structures (accepted for publication, 2005)Google Scholar
  2. 2.
    Bock, H.H., Diday, E.: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (2000)Google Scholar
  3. 3.
    Ciampi, A., Diday, E., Lebbe, J., Perinel, E., Vignes, R.: Growing a tree classifier with imprecise data. Pattern Recognition Leters 21, 787–803 (2000)CrossRefGoogle Scholar
  4. 4.
    D’Oliveira, S., De Carvalho, F.A.T., Souza, R.M.C.R.: Classification of sar images through a convex hull region oriented approach. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 769–774. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    De Carvalho, F.A.T.: Histograms in symbolic data analysis. Annals of Operations Research 55, 299–322 (1995)MATHCrossRefGoogle Scholar
  6. 6.
    Ichino, M., Yaguchi, H., Diday, E.: A fuzzy symbolic pattern classifier. In: Diday, E., et al. (eds.) Ordinal and Symbolic Data Analysis, pp. 92–102. Springer, Heidelberg (1996)Google Scholar
  7. 7.
    Krichevsky, R.E., Trofimov, V.K.: The performance of universal enconding. IEEE Transactions Information Theory IT-27, 199–207 (1981)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Mali, K., Mitra, S.: Symbolic classification, clustering and fuzzy radial basis function network. Fyzzy sets and systems 152, 553–564 (2005)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Prudencio, R.B.C., Ludermir, T.B., De Carvalho, F.A.T.: A modal symbolic classifier for selecting time series models. Pattern Recognition Leters 25, 911–921 (2004)CrossRefGoogle Scholar
  10. 10.
    Rossi, F., Conan-Guez, B.: Multi-layer perceptrom interval data. In: Bock, H.H. (ed.) Classification, Clustering and Data Analysis (IFCS 2002), pp. 427–434. Springer, Heidelberg (2002)Google Scholar
  11. 11.
    Souza, R.M.C.R., De Carvalho, F.A.T., Frery, A.C.: Symbolic approach to SAR image classification. In: IEEE International Geoscience and Remote Sensing Symposium, Hamburgo, pp. 1318–1320 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fabio C. D. Silva
    • 1
  • Francisco de A.T. de Carvalho
    • 1
  • Renata M. C. R. de Souza
    • 1
  • Joyce Q. Silva
    • 1
  1. 1.Centro de Informatica – CIn / UFPERecifeBrasil

Personalised recommendations