Multiple Classifiers for Graph of Words Embedding

  • Jaume Gibert
  • Ernest Valveny
  • Oriol Ramos Terrades
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)


During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.


Feature Vector Node Attribute Vocabulary Size Feature Subset Selection Borda Count 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jaume Gibert
    • 1
  • Ernest Valveny
    • 1
  • Oriol Ramos Terrades
    • 1
  • Horst Bunke
    • 2
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.Institute for Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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