Active Learning From Stream Data Using Optimal Weight Classifier Ensemble

doi:10.1109/TSMCB.2010.2042445

	Active Learning From Stream Data Using Optimal Weight Classifier Ensemble
	Zhu, Xingquan 1,2; Zhang, Peng 3; Lin, Xiaodong 4; Shi, Yong 5
	2010-12-01
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
ISSN	1083-4419
卷号	40 期号:6 页码:1607-1621
摘要	In this paper, we propose a new research problem on active learning from data streams, where data volumes grow continuously, and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. To tackle the technical challenges raised by the dynamic nature of the stream data, i.e., increasing data volumes and evolving decision concepts, we propose a classifier-ensemble-based active learning framework that selectively labels instances from data streams to build a classifier ensemble. We argue that a classifier ensemble's variance directly corresponds to its error rate, and reducing a classifier ensemble's variance is equivalent to improving its prediction accuracy. Because of this, one should label instances toward the minimization of the variance of the underlying classifier ensemble. Accordingly, we introduce a minimum-variance (MV) principle to guide the instance labeling process for data streams. In addition, we derive an optimal-weight calculation method to determine the weight values for the classifier ensemble. The MV principle and the optimal weighting module are combined to build an active learning framework for data streams. Experimental results on synthetic and real-world data demonstrate the performance of the proposed work in comparison with other approaches.
关键词	Active learning classifier ensemble stream data
DOI	10.1109/TSMCB.2010.2042445
收录类别	SCI
语种	英语
资助项目	Australia Discovery Grant[DP1093762] ; National Science Foundation of China (NSFC)[60674109] ; National Science Foundation of China (NSFC)[70621001] ; National Science Foundation of China (NSFC)[70531040] ; Chinese Ministry of Science and Technology[2004CB720103]
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000284364400016
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：82[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/2XEOYT63/12255
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Xingquan
作者单位	1.Florida Atlantic Univ, Dept Comp Sci & Engn, Boca Raton, FL 33431 USA 2.Univ Technol Sydney, Fac Engn & Informat Technol, QCIS Ctr, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100090, Peoples R China 4.Rutgers State Univ, Rutgers Business Sch, Dept Management Sci & Informat Syst, Newark, NJ 07102 USA 5.Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68118 USA
推荐引用方式 GB/T 7714	Zhu, Xingquan,Zhang, Peng,Lin, Xiaodong,et al. Active Learning From Stream Data Using Optimal Weight Classifier Ensemble[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2010,40(6):1607-1621.
APA	Zhu, Xingquan,Zhang, Peng,Lin, Xiaodong,&Shi, Yong.(2010).Active Learning From Stream Data Using Optimal Weight Classifier Ensemble.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,40(6),1607-1621.
MLA	Zhu, Xingquan,et al."Active Learning From Stream Data Using Optimal Weight Classifier Ensemble".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 40.6(2010):1607-1621.