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Omaha, Nebraska, USA
Oct. 28, 2007 to Oct. 31, 2007
ISBN: 0-7695-3018-4
pp: 757-762
ABSTRACT
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. *
CITATION
Xingquan Zhu, Peng Zhang, Xiaodong Lin, Yong Shi, "Active Learning from Data Streams", ICDM, 2007, 2013 IEEE 13th International Conference on Data Mining, 2013 IEEE 13th International Conference on Data Mining 2007, pp. 757-762, doi:10.1109/ICDM.2007.101
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