时间序列早期分类的多分类器集成方法  

Ensemble learning of a multi-classifier for early classification of time series

作　　者：李小斌[1] 李世银[2] 

机构地区：[1]徐州师范大学计算机科学与技术学院,江苏徐州221116 [2]中国矿业大学信息与电气工程学院,江苏徐州221008

出　　处：《山东大学学报（工学版）》2011年第4期73-78,共6页Journal of Shandong University（Engineering Science）

基　　金：国家高技术发展研究计划(863计划)重点资助项目(2008AA062200);江苏省产学研联合创新资金资助项目(BY2009114);徐州师范大学校重点基金资助项目(10XLA13)

摘　　要：为解决时间序列的早期分类问题,提出一种顺序子空间堆叠泛化(sequential subspace stacked generation,SSSG)的多分类器集成方法。该方法利用滑动窗口将时间序列划分成顺序子空间,采用多个一级分类器在顺序子空间上进行学习,输出各个子空间可能类别的概率,利用二级学习算法在这些概率结果上进行训练得到第二级的分类器。通过两级分类器可以判断已经出现部分特征的时间序列的类别,理论分析了影响分类性能的几个因素,仿真实验证明了本文算法的有效性。To solve the early classification for time series in some time-sensitive application area,an ensemble learning method named sequential subspace stacked generation（SSSG） was introduced.This method split time series into several sequential subspaces with slider windows.Multi first-layer classifiers were used on these sequential subspaces and label probability for these subspaces was generated.Then these probability results were input for the second layer classifier.The time series＇ label could be predicated by the two-layer classifier.Experimental results showed that this method could both do early classification for time series and achieve higher classification accuracy than only one classifier.

关 键 词：时间序列 集成学习 分类 顺序子空间 堆叠泛化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]