Robust ensemble learning for mining noisy data streams

doi:10.1016/j.dss.2010.11.004

	Robust ensemble learning for mining noisy data streams
	Zhang, Peng 1; Zhu, Xingquan 2; Shi, Yong 3,4; Guo, Li 1; Wu, Xindong 5,6
	2011
发表期刊	DECISION SUPPORT SYSTEMS
ISSN	0167-9236
卷号	50 期号:2 页码:469-479
摘要	In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction model from noisy stream data to accurately predict future samples. For noisy data sources, most existing works rely on data preprocessing techniques to cleanse noisy samples before the training of decision models. In data stream environments, these data preprocessing techniques are, unfortunately, hard to apply, mainly because the concept drifting in a data stream may make it very difficult to differentiate noise from samples of changing concepts. Accordingly, we propose an aggregate ensemble (AE) learning framework. The aim of AE is to build a robust ensemble model that can tolerate data errors. Theoretical and empirical studies on both synthetic and real-world data streams demonstrate that the proposed AE learning framework is capable of building accurate classification models from noisy data streams. (C) 2010 Elsevier B.V. All rights reserved.
关键词	Data stream Classification Ensemble learning Noise Concept drifting
DOI	10.1016/j.dss.2010.11.004
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China (NSFC)[61003167] ; National Science Foundation of China (NSFC)[60828005] ; National Science Foundation of China (NSFC)[70621001] ; National Science Foundation of China (NSFC)[70921061] ; China 973 Project[2007CB311100] ; Chinese Academy of Sciences (Overseas Collaboration Group) ; US National Science Foundation[CCF-0905337] ; Australian ARC[DP1093762]
WOS研究方向	Computer Science ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Operations Research & Management Science
WOS记录号	WOS:000286851300011
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：53[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/13137
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Peng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China 4.Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA 5.Hefei Univ Technol, Sch Comp Sci & Informat Eng, Hefei 230009, Peoples R China 6.Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA
推荐引用方式 GB/T 7714	Zhang, Peng,Zhu, Xingquan,Shi, Yong,et al. Robust ensemble learning for mining noisy data streams[J]. DECISION SUPPORT SYSTEMS,2011,50(2):469-479.
APA	Zhang, Peng,Zhu, Xingquan,Shi, Yong,Guo, Li,&Wu, Xindong.(2011).Robust ensemble learning for mining noisy data streams.DECISION SUPPORT SYSTEMS,50(2),469-479.
MLA	Zhang, Peng,et al."Robust ensemble learning for mining noisy data streams".DECISION SUPPORT SYSTEMS 50.2(2011):469-479.