Designing Optimized Pattern Recognition Systems By Learning Voronoi Vectors Using Genetic Algorithms
Price
Free (open access)
Volume
25
Pages
10
Published
2000
Size
874 kb
Paper DOI
10.2495/DATA000401
Copyright
WIT Press
Author(s)
Claudio M.N.A. Pereira and Roberto Schirru
Abstract
Designing optimized pattern recognition systems by learning Voronoi vectors using genetic algorithms Claudio M. N. A. Pereira ^ and Roberto Schirru * ' Institute de Engenharia Nuclear, CREA, Comissdo National de Energia Nuclear, Brazil ^ Programa de Engenharia Nuclear, Universidade Federal do Rio de Janeiro, Brazil. * Computer Science Department, Universidade Igua$u, Brazil. Abstract In this work is described a methodology for developing optimized pattern recognition systems by means of genetic machine learning. The idea is to redefine the set of classes that must be learned by the classification system, mapping them into another set which the Voronoi vectors can successfully classify a sample into one of the original classes. The main objective of this approach is to find the minimum number of classification rules (by minimizing the number of classes in the new set) that maximizes the number of correct classifications. To accomplish that, a genetic algorithm was design. In
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