Classifier Based on Non-negative Matrix Factorization for Tumor Data Classification

摘要：

With the development of DNA microarrys technology, it is very important to classify the different tumor types correctly in cancer diagnosis and drug discovery. In this paper, we discuss how to use the nonnegative matrix factorization (NMF) to extract features and illustrate how to adopt classification model to improve the classification accuracy. For the DNA microarrys, the gene expression data is firstly preprocessed for normalization. NMF is then applied to extract features. Finally, we use the Back Propagation Neural Network (BPNN) as the classifier to classify the different samples. In our experiments, we adopt the leukemia and colon datasets to test the validity. The experimental results show that the proposed method yields a good recognition rate.

关键词： leukemia and colon datasets nonnegative matrix factorization DNA microarrys Back Propagation Neural Network

作者: Chen Yuehui Xing Xifeng Xu Jingru

作者单位: University of Jinan, School of Information Science and Engineering, Jinan, Shandong, 250022, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 935-938

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）