Research of Classification Hypersurface Basde on Tangent CIRCULAR ARC SMOOTH SVM (TAC-SSVM)

摘要：

The lack of efficient heuristic information is the fundamental reason that causes the low effectiveness of currently used approaches for extracting symbolic rules. Classification hypersurface are direct heuristic information for selecting attributes. In SVM (Support Vector Machine), classification hypersurface is emphasized because of the direct induction of the support vectors. In this paper, a new SVM model and a new measurement method of the classification power of attributes on the basis of the characteristics of the classification hypersurfaces based on the proposed SVM model is presented. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approach.

作者: YAN-FENG FAN DE-XIAN ZHANG

作者单位: College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China

会议类型: 国际会议

会议名称: 2008 IEEE International Symposium on IT in Medicine and Education(2008信息技术在医学和教育中的应用国际研讨会)(ITME 2008)

会议地点: 厦门

会议语种:英文

页码: 960-963

在线出版日期: 2008-12-12（万方平台首次上网日期，不代表论文的发表时间）