Diagnosis of Breast Cancer Tumor Based on Manifold Learning and Support Vector Machine

摘要：

This paper proposes an efficient algorithm based on manifold learning and Support Vector Machine(SVM)for the diagnosis of breast cancer tumor.First,Isomap algorithm is implemented to Project high-dimensional breast tumor data to much lower dimensional space,then the processed data are classified by the SVM.Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method can greatly speed up the training and testing of the classifier and get high testing correct rate,superior to the classical Principal Component Analysis(PCA)algorithm.

作者: Zhaohui Luo Xiaoming Wu Shengwen Guo Binggang Ye

作者单位: College of Biology Science and Engineering South China University of Technology Department of Biomedical Engineering,South China University of technology the south campus510006

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 703-707

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）