Support Vector Machine Methods for the Prediction of Cancer Growth

摘要：

In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM.

作者: Xi Chen Wai-Ki Ching Kiyoko F. Aoki-Kinoshita Koh Furuta

作者单位: The Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of H Department of Bioinformatics Faculty of Engineering Soka University Tokyo, Japan Division of Clinical Laboratories National Cancer Center Hospital Tokyo, Japan

会议类型: 国际会议

会议名称: The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

会议地点: 黄山

会议语种:英文

页码: 229-232

在线出版日期: 2010-05-28（万方平台首次上网日期，不代表论文的发表时间）