Using Rough Set to Reduce SVM Classifier Complexity and Its Use in SARS Data Set

摘要：

　　For SVM classifier, Pre-selecting data is necessary to achieve satisfactory classification rate and reduction of complexity.According to Rough Set Theory, the examples in boundary region of a set belong to two or more classes, lying in the boundary of the classes, and according to SVM, support vectors lie in the boundary too.So we use Rough Set Theory to select the examples of boundary region of a set as the SVM classifier set.the complexity of SVM classifier would be reduced and the accuracy maintained.Experiment results of SARS data indicate that our schema is available in both the training and prediction stages.

作者: Feng Honghai Liu Baoyan Yin Cheng Li Ping Yang Bingru Chen Yumei

作者单位: Urban & Rural Construction School,Hebei Agricultural University071001 Baoding,China;Information Engi China Academy of Traditional Chinese Medicine,100700 Beijing,China Modem Educational Center,Hebei Agricultural University071001 Baoding,China Information Engineering School,University of Science and Technology Beijing100083 Beijing,China Tian”e Chemical Fiber Company of Hebei Baoding071000 Baoding,China

会议类型: 国内会议

会议名称: ”数字化中医信息系统“临床术语本体研究专家研讨会

会议地点: 北京

会议语种:英文

页码: 575-580

在线出版日期: 2014-09-01（万方平台首次上网日期，不代表论文的发表时间）