Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm / 浙江大学学报(英文版)(B辑:生物医学和生物技术)
Journal of Zhejiang University. Science. B
;
(12): 961-973, 2005.
Artículo
en Inglés
| WPRIM
| ID: wpr-263272
ABSTRACT
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Algoritmos
/
Neoplasias de la Mama
/
Leucemia Mieloide Aguda
/
Valor Predictivo de las Pruebas
/
Análisis de Secuencia por Matrices de Oligonucleótidos
/
Genes
/
Genética
/
Métodos
/
Modelos Genéticos
Tipo de estudio:
Estudio pronóstico
Límite:
Femenino
/
Humanos
Idioma:
Inglés
Revista:
Journal of Zhejiang University. Science. B
Año:
2005
Tipo del documento:
Artículo
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