Study of improving Support Vector Machine algorithm based on medical data mining

摘要：

　　This paper introduces fundamental theory and mathematic model of Support Vector Machine(SVM),and also covers applying SVM algorithm in data assorting.In conventional SVM model,sample set always has noisy and isolated points,for solving this problem this paper proposes a SVM boundary sample cut algorithm:first,pre-select boundary samples,then apply Remove-Only algorithm to remove some inappropriate points,then the result will be final sample set for SVM.At last,we compared conventional and improved algorithms by applying them on categorizing two medical data sets; the accuracy of improved algorithm achieves 100%.The result shows this improved algorithm is with significant practical advantage and value.

关键词： data mining boundary sample SVM

作者: WU Wei

作者单位: Department of Computer and Information EngineeringZhejiang Water Conservancy and Hydropower CollegeHangzhou, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1780-1784

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）