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

被引：0

作者：

Raveaux, Romain ^{[1
]}

Eugen, Barbu ^{[2
]}

Locteau, Herve ^{[2
]}

Adam, Sebastien ^{[2
]}

Heroux, Pierre ^{[2
]}

Trupin, Eric ^{[2
]}

机构：

[1] Univ La Rochelle, L3I Lab, La Rochelle, France

[2] Univ Rouen, LITIS Labs, F-76821 Mont St Aignan, France

来源：

GRAPH-BASED REPRESENTATIONS IN PATTERN RECOGNITION, PROCEEDINGS | 2007年 / 4538卷

关键词：

graph classification; multi-objective optimization; machine learning; graph dissimilarity measure;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：361 / +

页数：3

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