Graph Classification Using Genetic Algorithm and Graph Probing Application to Symbol Recognition

Authors:
Eugen Barbu;Romain Raveaux;Herve Locteau;Sebastien Adam;Pierre Heroux;Eric Trupin
Affiliations:
LITIS Labs - University of Rouen, FRANCE;LITIS Labs - University of Rouen, FRANCE;LITIS Labs - University of Rouen, FRANCE;LITIS Labs - University of Rouen, FRANCE;LITIS Labs - University of Rouen, FRANCE;LITIS Labs - University of Rouen, FRANCE
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

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Cited 1

GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

We present in this paper a graph classification approach using genetic algorithm and a fast dissimilarity measure between graphs called graph probing. The approach consists in the learning of a set of synthetic graph prototypes which are used for a 1NN classification step. Some 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.