A Graph Classification Approach Using a Multi-objective Genetic Algorithm Application to Symbol Recognition

  • Romain Raveaux
  • Barbu Eugen
  • Hervé Locteau
  • Sébastien Adam
  • Pierre Héroux
  • Eric Trupin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4538)

Abstract

In this paper, a graph classification approach based on a multi-objective genetic algorithm is presented. The method consists in the learning of sets composed of synthetic graph prototypes which are used for a classification step. These learning graphs are generated by simultaneously maximizing the recognition rate while minimizing the confusion rate. Using such an approach the algorithm provides a range of solutions, the couples (confusion, recognition) which suit to the needs of the system. Experiments are performed on real data sets, representing 10 symbols. These tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.

Keywords

graph classification multi-objective optimization machine learning graph dissimilarity measure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Romain Raveaux
    • 1
  • Barbu Eugen
    • 2
  • Hervé Locteau
    • 2
  • Sébastien Adam
    • 2
  • Pierre Héroux
    • 2
  • Eric Trupin
    • 2
  1. 1.L3I Laboratory – University of La RochelleFrance
  2. 2.LITIS Labs – University of RouenFrance

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