Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data.
BMC Bioinformatics
; 7 Suppl 4: S15, 2006 Dec 12.
Article
em En
| MEDLINE
| ID: mdl-17217507
ABSTRACT
BACKGROUND:
Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance computing techniques.RESULTS:
In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM) have been developed based on the sequential minimum optimization-type decomposition method for support vector machines (SMO-SVM). It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster for multicategory classification of microarray data. PMC-SVM has been analyzed and evaluated using four microarray datasets with multiple diagnostic categories, such as different cancer types and normal tissue types.CONCLUSION:
The experiments show that the PMC-SVM can significantly improve the performance of classification of microarray data without loss of accuracy, compared with previous work.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Automatizado de Padrão
/
Biomarcadores Tumorais
/
Análise por Conglomerados
/
Análise de Sequência com Séries de Oligonucleotídeos
/
Perfilação da Expressão Gênica
/
Proteínas de Neoplasias
/
Neoplasias
Tipo de estudo:
Diagnostic_studies
/
Evaluation_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Estados Unidos