%0 Journal Article
%T Classifying data streams by stacking ensemble<br>基于堆叠集成的数据流分类*
%A LIANG Chun-quan
%A ZHANG Yang
%A LIU Quan-zhong
%A <br>梁春泉
%A 张阳
%A 刘全中
%J 计算机应用研究
%D 2009
%I 
%X Ensemble learning is a general method for classifying data streams. In order to get a better classification, this paper proposed a general framework for classifying data streams by stacking ensemble. Built another classifier to combine base classifiers. Experiments show that comparing majority vote or weight vote ensemble classifiers, stacking ensemble classifiers has stronger ability in adapting to concept drifting and higher accuracy.
%K stacking ensemble
%K classifying data streams
%K concept drifting<br>堆叠集成
%K 数据流分类
%K 概念漂移
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=0589666E842E5E7CE4988C7460E826C1&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=94C357A881DFC066&sid=ADAF655E679AEBA1&eid=A5884B9A69EE562A&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11