Extraction method of the visual graphical feature from biomedical data / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 916-921, 2011.
Article
en Zh
| WPRIM
| ID: wpr-359153
Biblioteca responsable:
WPRO
ABSTRACT
The vector space transformations such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA) or the kernel-based methods may be applied on the extracted feature from the field, which could improve the classification performance. A barycentre graphical feature extraction method of the star plot was proposed in the present study based on the graphical representation of multi-dimensional data. The feature order question of the graphical representation methods affecting the star plot was investigated and the feature order method was proposed based on the improved genetic algorithm (GA). For some biomedical datasets, such as breast cancer and diabetes, the obtained classification error of barycentre graphical feature of star plot in the GA based optimal feature order is very promising compared to the previously reported classification methods, and is superior to that of traditional feature extraction method.
Texto completo:
1
Base de datos:
WPRIM
Asunto principal:
Algoritmos
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Gráficos por Computador
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Reconocimiento de Normas Patrones Automatizadas
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Inteligencia Artificial
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Análisis Discriminante
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Modelos Lineales
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Recolección de Datos
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Análisis de Componente Principal
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Investigación Biomédica
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Métodos
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Año:
2011
Tipo del documento:
Article