Title :
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
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;
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6916-1
DOI :
10.1109/IJCNN.2010.5596657
