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基于实例加权方法的概念漂移问题研究
引用本文:胡学钢,潘春香.基于实例加权方法的概念漂移问题研究[J].计算机工程与应用,2008,44(21):188-191.
作者姓名:胡学钢  潘春香
作者单位:合肥工业大学,计算机与信息学院,合肥,230009
基金项目:国家自然科学基金 , 安徽省自然科学基金
摘    要:数据流上的漂移概念发现已成为数据挖掘领域的研究热点之一。针对存在概念漂移的数据流分类问题,提出一种基于实例加权方法的数据流分类算法(EWAMDS),根据基分类器在训练实例上的分类结果调整该实例的权值,以增强漂移实例在新分类器中的影响,同时引入动态的权值修改因子以提高算法的适应性。实验结果表明,动态地调整实例的权值时算法的适应性更强;与weighted-bagging相比,EWAMDS的时间开销显著降低、分类正确率显著提高。

关 键 词:数据流  概念漂移  集成分类器  分类
收稿时间:2008-4-30
修稿时间:2008-5-29  

Study of example-weighted method for tracking concept drift
HU Xue-gang,PAN Chun-xiang.Study of example-weighted method for tracking concept drift[J].Computer Engineering and Applications,2008,44(21):188-191.
Authors:HU Xue-gang  PAN Chun-xiang
Affiliation:School of Computer and Information,Hefei University of Technology,Hefei 230009,China
Abstract:The tracking of drifting concept from data streams has recently become one of hot spots in data mining.In this paper,a Example-Weighted algorithm for mining data streams (EWAMDS) is proposed for data streams classification in the presence of concept drift,in which weight of train example is adjusted according to base classifier's prediction on it,so as to enhance influence of drifting examples in new classifier,and a dynamic weight modifying factor is introduced to improve the adaptability of this algorithm.The results of experiments indicate that modifying weight of example dynamically makes this algorithm more adaptively;and in comparison with weighted-bagging,EWAMDS has a lower time consumption and higher accuracy.
Keywords:data streams  concept drifts  ensemble classifier  classification
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