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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)

Abstract

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.

Keywords

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.

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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

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