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2013 IEEE 13th International Conference on Data Mining (2007)
Omaha, Nebraska, USA
Oct. 28, 2007 to Oct. 31, 2007
ISSN: 1550-4786
ISBN: 0-7695-3018-4
pp: 757-762
In this paper, we address 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 newly arrived instances as accurate as possible. In order to tackle the challenges raised by data streams' dynamic nature, we propose a classifier ensembling based active learning framework which selectively labels instances from data streams to build an accurate classifier. A Minimal Variance principle is introduced to guide instance labeling from data streams. In addition, a weight updating rule is derived to ensure that our instance labeling process can adaptively adjust to dynamic drifting concepts in the data. Experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches. *
Peng Zhang, Xiaodong Lin, Xingquan Zhu, Yong Shi, "Active Learning from Data Streams", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 757-762, 2007, doi:10.1109/ICDM.2007.101
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