Mining Multi-Label Data Streams Using Ensemble-Based Active Learning

Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused on mining from single-label data streams. Mining from multi-label data streams has not been fully addressed yet. On the other hand, although some recent work touched the multi-label stream mining problem, they never consider the expensive labeling cost issue, preventing them from real-world applications. To this end, we study, in this paper, a challenging problem that mining from multi-label data streams with limited labeling resource. Specifically, we propose an ensemblebased active learning framework to handle the large volume of stream data, expensive labeling cost and concept drifting problems on multi-label data streams. Experiments on both synthetic and real world data sets demonstrate the performance of the proposed method.