关键词: DNA微阵列, 特征提取, 独立成分分析（ICA）, 聚类分析, 支持向量机（SVMs）

Abstract: Gene sets of interest typically selected by usual ranking methods from DNA microarray data will contain many highly correlated genes，and using the evaluating method of single gene does not reflect really the capacity of classifier of character sets.And disease diagnostics based on gene expression microarray data presents another major challenge due to the number of genes far exceeding the number of samples.So a method of extracting DNA microarray data features for the tissue classification is proposed.The method makes use of K-means to cluster analysis for genes，getting the DNA microarray data centers of every subclass，then uses ranking methods to get grid of the genes not useful for classification.Then，the features of the remaining subclass sets are extracted by ICA，thus a classifier is structured by SVMs for tissues classification.Real biological data experiments show that the method can evaluate the classification capacity of genes，decrease the number of features and increase the classification accuracy of the existing classifiers by extracting a compound gene.

Key words: DNA microarray, extracting feature, Independent Components Analysis（ICA）, clustering analysis, Support Vector Machines（SVMs）