A graph classification approach using a multi-objective genetic algorithm application to symbol recognition

Authors:
Romain Raveaux;Barbu Eugen;Hervé Locteau;Sébastien Adam;Pierre Héroux;Eric Trupin
Affiliations:
L3I Laboratory, University of La Rochelle, 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:
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Year:
2007

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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.