Li Jing, Wang Jinjia, Hong Wenxue
College of Science, Yanshan University, Qinhuangdao 066004, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Oct;28(5):916-21.
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.
诸如主成分分析（PCA）、线性判别分析（LDA）、独立成分分析（ICA）等向量空间变换或基于核的方法可应用于从该领域提取的特征上，这可能会提高分类性能。本研究基于多维数据的图形表示提出了一种星图的重心图形特征提取方法。研究了影响星图的图形表示方法的特征排序问题，并基于改进的遗传算法（GA）提出了特征排序方法。对于一些生物医学数据集，如乳腺癌和糖尿病数据集，与先前报道的分类方法相比，基于GA的最优特征排序下星图重心图形特征的分类误差非常可观，且优于传统特征提取方法。
