Outlier Detection Method Based on SVM and Its Application in Copper-matte Converting

摘要：

Outlier detection can be treated as a part of the data preprocess or as the object of data mining. There is still no effective detection method for the high-dimensional nonlinear outlier samples. This paper presents an outlier detection method based on support vector machine (SVM). A SVM model built by the clean sample set without outlier is used to predict the samples, when the error between the prediction-value and actual value exceeds the threshold, the sample is taken as an outlier, otherwise a normal one. The present outlier detection method has been applied to analyze the practical copper-matte converting production data. The results show that this method can efficiently and correctly detect the high dimensional nonlinear outlier sample and has considerable practical value.

关键词： Data Mining Outlier Detection Support Vector Machine Copper-matte Converting

作者: Xiaoqi Peng Jun Chen Hongyuan Shen

作者单位: School of Information Science and Engineering, Central South University, Changsha, 410083, China Dep School of Information Science and Engineering, Central South University, Changsha, 410083, China Ins Institute of Information and Electrical Engineering, Hunan University of Science and Technology, Xia

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 628-631

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）