Active Learning From Stream Data Using Optimal Weight Classifier Ensemble
文献类型：期刊论文
| 作者 | Zhu, Xingquan1,2; Zhang, Peng3; Lin, Xiaodong4; Shi, Yong5 |
| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
 |
| 出版日期 | 2010-12-01 |
| 卷号 | 40期号:6页码:1607-1621 |
| 关键词 | Active learning classifier ensemble stream data |
| ISSN号 | 1083-4419 |
| DOI | 10.1109/TSMCB.2010.2042445 |
| 英文摘要 | 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 classifier-ensemble-based active learning framework that selectively labels instances from data streams to build a classifier ensemble. We argue that a classifier ensemble's variance directly corresponds to its error rate, and reducing a classifier ensemble's variance is equivalent to improving its prediction accuracy. Because of this, one should label instances toward the minimization 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 calculation method 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. |
| 资助项目 | Australia Discovery Grant[DP1093762] ; National Science Foundation of China (NSFC)[60674109] ; National Science Foundation of China (NSFC)[70621001] ; National Science Foundation of China (NSFC)[70531040] ; Chinese Ministry of Science and Technology[2004CB720103] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000284364400016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/12254]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhu, Xingquan |
| 作者单位 | 1.Florida Atlantic Univ, Dept Comp Sci & Engn, Boca Raton, FL 33431 USA 2.Univ Technol Sydney, Fac Engn & Informat Technol, QCIS Ctr, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100090, Peoples R China 4.Rutgers State Univ, Rutgers Business Sch, Dept Management Sci & Informat Syst, Newark, NJ 07102 USA 5.Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68118 USA |
| 推荐引用方式 GB/T 7714 | Zhu, Xingquan,Zhang, Peng,Lin, Xiaodong,et al. Active Learning From Stream Data Using Optimal Weight Classifier Ensemble[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2010,40(6):1607-1621. |
| APA | Zhu, Xingquan,Zhang, Peng,Lin, Xiaodong,&Shi, Yong.(2010).Active Learning From Stream Data Using Optimal Weight Classifier Ensemble.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,40(6),1607-1621. |
| MLA | Zhu, Xingquan,et al."Active Learning From Stream Data Using Optimal Weight Classifier Ensemble".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 40.6(2010):1607-1621. |
入库方式： OAI收割
来源：计算技术研究所
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