Mining Multi-label Data Streams Using Ensemble-based Active Learning
- Publisher:
- SIAM
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the 2012 SIAM International Conference on Data Mining, 2012, pp. 1131 - 1140
- Issue Date:
- 2012-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2013005165OK.pdf | 443.45 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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.
Please use this identifier to cite or link to this item: