A dynamic subspace learning method for tumor classification using microarray gene expression data

摘要：

Among most of the subspace learning methods, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are classic ones. PCA tries to maximize the total scatter across all classes. In this case, however, the data set, with a small between-class scatter and a large within-class scatter, can also have a large total scatter. It conflicts with Maximum Margin Criterion (MMC)which tries to maximize the between-class scatter and minimize the within-class scatter. To address the conflict problem, we proposed a dynamic subspace learning method which can balance the objectives of PCA and MMC simultaneously by searching for the best coefficient. Our experiments are implemented by classification on two tumor microarray datasets. Firstly a simple t-test was used for gene selection, then our novel method was applied to gene extraction, and finally we adopted KNN and SVM classifiers to evaluate the effectiveness of our method. Results show that the new feature extractors are effective and stable.

关键词： dynamic subspace learning method dimension reduction tumor classification microarray gene expression data

作者: Yaru Su Rujing Wang Chuanxi Li Peng Chen

作者单位: institute of Intelligent Machines, Chinese Academy of Science, Hefei 230031, P.R. China School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, P.R.China

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 407-411

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）