Using Simple Gaussian Mizture Model for Multiclass Classification Based on Tumor Gene Ezpression Data

摘要：

In this paper, we developed a novel multi-class classification method combining the ideal of discriminant analysis and Gaussian Mixture Model. Different from binary classification, this method reserves more information and is useful for multi-class tumor subtypes diagnosis and treatment. Four datasets, ALL-AML-3, ALL-AML-3, MLL and ALL, were collected and used to evaluate the prediction performance. The classification accuracies are all about 2.5％ higher than KNN classifier and comparable well to SVM for leave-one-out cross validation. The results demonstrate that this method is simple and efficient even more less computational cost. It is a useful tool for multi-class tumor classification.

关键词： gene ezpression data multi-class classification Simple Gaussian Mizture Model K-nearest neighbor support vector machine bioinformatics

作者: Wenlong Xu Xianghua Zhang Huanqing Feng

作者单位: Department of Electronic Science and Technology University of Science and Technology of China Hefei 230027, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 470-473

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）