生物医学数据的可视化图形特征提取方法

Extraction Method of the Visual Graphical Feature from Biomedical Data

从领域问题中提取的原始特征进行某种矢量空间变换如主成分分析(PCA)、线性判别分析(LDA)、独立分量分析(ICA)和基于核的方法可能会改进分类器分类性能。基于多元数据的星点图表示方法,提出了一种新的可视化的图形特征提取方法———星点图重心图形特征。对星点图图表示中存在的特征排序影响图表示的问题进行了研究,提出了基于改进的遗传算法(GA)的特征排序。乳腺癌和糖尿病等生物医学数据集在遗传算法特征排序下的重心图形特征的分类结果都超过了公认的模式识别网站上的报道,也优于传统的矢量空间的特征提取方法下得到的特征的分类性能。

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 baryeentre 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.