面向非静态数据分类的演进支持向量机  

Research on Evolving Support Vector Machines for Nonstationary Data Classification

作　　者：史荧中[1,2] 王士同[1] 张景祥[1] 倪彤光[1,3] 

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]无锡职业技术学院物联网技术学院,无锡214121 [3]常州大学信息科学与工程学院,常州213164

出　　处：《电子与信息学报》2013年第6期1413-1420,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61170122,61272210,61202311);江苏省自然科学基金(BK2012552);江苏省研究生创新基金(CXZZ12-0759)资助课题

摘　　要：时间自适应支持向量机(TA-SVM)方法在处理非静态数据集时表现出良好的性能,但仅根据邻接子分类器相似而获得的相关信息并不充分,由此可能会导致训练所得模型不可靠,限制其应用能力。该文通过定义子分类器序列的相关性衰减函数,提出新的面向非静态数据分类问题的演进支持向量机(Evolving Support VectorMachines,ESVM)。ESVM使用衰变函数以体现子分类器之间的相关程度,通过约束所有子分类器之间的带权差异以求得变化更光滑的子分类器序列,契合了数据中隐藏的渐变概念。在各种数据缓慢变化场景的对比实验中,该文的ESVM方法优于TA-SVM方法。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.

关 键 词：支持向量机 分类器序列 非静态数据 演进 衰变函数 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]