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

Peng Wang, Peng Zhang, Li Guo

Abstract: 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 ensemble-based 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.