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Graph-Based Multiple Classifier Systems A Data Level Fusion Approach

  • Michel Neuhaus
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

The combination of multiple classifiers has been successful in improving classification accuracy in many pattern recognition problems. For graph matching, the fusion of classifiers is normally restricted to the decision level. In this paper we propose a novel fusion method for graph patterns. Our method detects common parts in graphs in an error-tolerant way using graph edit distance and constructs graphs representing the common parts only. In experiments, we demonstrate on two datasets that the method is able to improve the classification of graphs.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Michel Neuhaus
    • 1
  • Horst Bunke
    • 1
  1. 1.Department of Computer ScienceUniversity of BernBernSwitzerland

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