Research on Evolving Support Vector Machines for Nonstationary Data Classification
Shi Ying-zhong①② Wang Shi-tong① Zhang Jing-xiang① Ni Tong-guang①③
①(School of Digital Media, Jiangnan University,Wuxi 214122 China) ②(School of Internet of Things Technology, Wuxi Institute of Technology, Wuxi 214121, China) ③(School of Information Science & Technology, Changzhou University, Changzhou 213164, China)
Abstract：The Time Adaptive Support Vector Machine (TA-SVM) algorithm exhibits good performance for nonstaionary datasets. However, insufficient information from adjacent subclassifier may lower the reliability of the obtained classification model and weakens its usefulness. A novel classifier named Evolving Support Vector Machines (ESVM) is proposed in this study by defining the relationship decaying function of the subclassifier serial. The evolving relationship between all the subclassifiers are considered in ESVM, thus a more smoothing subclassifier serial can be obtained by constraining the weighted variance between all subclassifiers, conforming with the characteristic of drifting concept hidden in the data. The effectiveness of the proposed ESVM is also experimentally verified.
史荧中, 王士同, 张景祥, 倪彤光. 面向非静态数据分类的演进支持向量机[J]. 电子与信息学报, 2013, 35(6): 1413-1420.
Shi Ying-Zhong, Wang Shi-Tong, Zhang Jing-Xiang, Ni Tong-Guang. Research on Evolving Support Vector Machines for Nonstationary Data Classification. , 2013, 35(6): 1413-1420.
2013, Vol. 35 
