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Robust ensemble learning for mining noisy data streams

文献类型：期刊论文

作者Zhang, Peng1; Zhu, Xingquan2; Shi, Yong3,4; Guo, Li1; Wu, Xindong5,6
刊名DECISION SUPPORT SYSTEMS
出版日期2011
卷号50期号:2页码:469-479
关键词Data stream Classification Ensemble learning Noise Concept drifting
ISSN号0167-9236
DOI10.1016/j.dss.2010.11.004
英文摘要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.
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
语种英语
WOS记录号WOS:000286851300011
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/13136]  
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.

入库方式： OAI收割

来源：计算技术研究所

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