Extraction method of the visual graphical feature from biomedical data.
- Author:
Jing LI
1
;
Jinjia WANG
;
Wenxue HONG
Author Information
1. College of Science, Yanshan University, Qinhuangdao 066004, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artificial Intelligence;
Biomedical Research;
Computer Graphics;
Data Collection;
Discriminant Analysis;
Linear Models;
Pattern Recognition, Automated;
methods;
Principal Component Analysis
- From:
Journal of Biomedical Engineering
2011;28(5):916-921
- CountryChina
- Language:Chinese
-
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.
