从因子分析的角度出发解决基因表达谱分析问题。为解决独立成分分析方法在求解过程中的不稳定性，提出一种基于选择性独立成分分析的DNA微阵列数据集成分类器。首先对基因表达水平的重构误差进行分析，选择部分重构误差较小的独立成分进行样本重构，然后基于重构后的样本同时训练多个支持向量机基分类器，最后选择部分分类正确率较高的基分类器进行最大投票以得到最终结果。在3个常用测试集上验证了本文设计方法的有效性。-This paper tries to deal with gene expression problem in view of factor analysis. In order to overcome the instability problem caused by performing independent component analysis, a DNA microarray data ensemble classifier based on selective independent component analysis is proposed. The reconstruction error of each gene is analyzed firstly and a part of independent components which contribute relatively small reconstruction errors are selected to reconstruct new samples. After that, several support vector machine base classifiers are trained simultaneously. Finally, the best base classifiers with high correct rates are selected to participate in the ensemble, using the majority voting method. Results on three publicly available microarray datasets show the feasibility and validity of the method proposed in this paper.