%0 Journal Article
%T A Double-window-based Classification Algorithm for Concept Drifting Data Streams<br>一种基于双层窗口的概念漂移数据流分类算法
%A ZHU Qun
%A ZHANG Yu-Hong
%A HU Xue-Gang
%A LI Pei-Pei
%A <br>朱群
%A 张玉红
%A 胡学钢
%A 李培培
%J 自动化学报
%D 2011
%I 
%X Tracking concept drifts in data streams has recently become a hot topic in data mining. Most of the existing work is built on a single-window-based mechanism to detect concept drifts. Due to the inherent limitation of the single-window-based mechanism, it is a challenge to handle different types of drifts. Motivated by this, a new classification algorithm based on a double-window mechanism for handling various concept drifting data streams (DWCDS) is proposed in this paper. In terms of an ensemble classifier in random decision trees, a double-window-based mechanism is presented to detect concept drifts periodically, and the model is updated dynamically to adapt to concept drifts. Extensive studies on both synthetic and real-word data demonstrate that DWCDS could quickly and efficiently detect concept drifts from streaming data, and the performance on the robustness to noise and the accuracy of classification is also improved significantly.
%K Data stream
%K concept drift
%K classification
%K random decision tree
%K sliding widow<br>数据流
%K 概念漂移
%K 分类
%K 随机决策树
%K 滑动窗口
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=2CF049F8B8CD37102CA47B3900D56464&yid=9377ED8094509821&vid=42425781F0B1C26E&iid=9CF7A0430CBB2DFD&sid=E6E6318AC4BCBFDB&eid=FBF817B1E8A20479&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=28