Active Learning From Stream Data Using Optimal Weight Classifier Ensemble

IEEE-Inst Electrical Electronics Engineers Inc
Publication Type:
Journal Article
Ieee Transactions On Systems Man And Cybernetics Part B-Cybernetics, 2010, 40 (6), pp. 1607 - 1621
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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 classifierensemble- based active learning framework that selectively labels instances from data streams to build a classifier ensemble. We argue that a classifier ensembleâs variance directly corresponds to its error rate, and reducing a classifier ensembleâs variance is equivalent to improving its prediction accuracy. Because of this, one should label instances toward theminimization 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 calculationmethod 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.
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