An genetic approach to Support Vector Machines in classification problems

Author

de A Padilha, Carlos Alberto ; Lima, Naiyan Hari C ; Neto, Adriao Duarte Doria ; De Melo, Jorge Dantas

Author_Institution

Comput. Eng. & Autom. Dept., Fed. Univ. of Rio Grande do Norte, Natal, Brazil

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

4

Abstract

There are a lot of different methods in pattern classification, in which one of the most popular is the Support Vector Machine. Lots of tools have been developed to improve SVM classification, mainly the development of new classifying methods and the employment of SVM ensembles. Meanwhile, evolutionary algorithms are recognized tools to solve optimization problems, and have in the genetic algorithm its most popular metaheuristic. So, in this paper, our proposal is to unite both techniques, applying a genetic algorithm to optimize the classification of a set of SVM, testing with some benchmark data sets.

Keywords

genetic algorithms; pattern classification; support vector machines; SVM classification; SVM ensembles; classification problems; classifying methods; evolutionary algorithms; genetic algorithm; optimization problems; pattern classification; recognized tools; support vector machines; Biological cells; Classification algorithms; Error analysis; Gallium; Genetic algorithms; Support vector machines; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2010 International Joint Conference on

Conference_Location

Barcelona

ISSN

1098-7576

Print_ISBN

978-1-4244-6916-1

Type

conf

DOI

10.1109/IJCNN.2010.5596657

Filename

5596657