Relevant Vector Machine Based on Gene Pre-selection for Cancer Microarray Expression Classification
DNA microarray technology can measure the expression levels of thousands of genes simultaneously. It has become an important tool in cancer biological investigations. In combination with classification methods, microarray technology can be useful to support clinical management decisions for individual patients. Cancer microarray expression classification is a typical case that has high dimensions and small samples. Many classification methods have been widely used in the study of classification of tumor microarray expression, such as, support vector machine (SVM), least squares support vector machine (LSSVM), and fisher discriminate analysis with dimensionality reduction and so on. In gene expression dataset, there are many genes that are redundant for cancer microarray expression classification. The most relevant gene selection is important issues. A robust two-step approach is presented. For reducing the computation complexity, a gene pre-selection procedure by ReliefF is adopted to reduce the huge number of genes being considered. Secondly, the relevance vector machine is used on the gene subset for cancer microarray expression classification. On two cancer microarray datasets, e.g. leukemia microarray dataset and colon cancer microarray dataset, the new approach is compared to the several existing methods. The experimental results show that the proposed approach can achieve high classification accuracy and is more robust.
Qiu Langbo Wang Zhengzhi Wang Guangyun
College of Mechatronics Engineering and Automation,National Univ. of Defense Technology, Changsha, H College of Mechatronics Engineering and Automation,National Univ. of Defense Technology, Changsha, H
国际会议
北京
英文
2007-08-05（万方平台首次上网日期，不代表论文的发表时间）