Online Active Learning for Drifting Data Streams
文献类型：期刊论文
| 作者 | Liu, Sanmin1,2; Xue, Shan2; Wu, Jia2; Zhou, Chuan3 ; Yang, Jian2; Li, Zhao4; Cao, Jie5
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2021-07-20 |
| 页码 | 15 |
| 关键词 | Labeling Data models Uncertainty Biological system modeling Computational modeling Cognition Adaptation models Active learning concept drift data stream classification online incremental learning |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2021.3091681 |
| 英文摘要 | Classification methods for streaming data are not new, but very few current frameworks address all three of the most common problems with these tasks: concept drift, noise, and the exorbitant costs associated with labeling the unlabeled instances in data streams. Motivated by this gap in the field, we developed an active learning framework based on a dual-query strategy and Ebbinghaus's law of human memory cognition. Called CogDQS, the query strategy samples only the most representative instances for manual annotation based on local density and uncertainty, thus significantly reducing the cost of labeling. The policy for discerning drift from noise and replacing outdated instances with new concepts is based on the three criteria of the Ebbinghaus forgetting curve: recall, the fading period, and the memory strength. Simulations comparing CogDQS with baselines on six different data streams containing gradual drift or abrupt drift with and without noise show that our approach produces accurate, stable models with good generalization ability at minimal labeling, storage, and computation costs. |
| 资助项目 | Nature Science Foundation of Anhui Province[1608085MF147] ; Humanities and Social Science Foundation of the Ministry of Education[18YJA630114] ; Major Project of Natural Science Research in Colleges and Universities of Anhui Province[KJ2019ZD15] ; National Natural Science Foundation of China[92046026] ; National Natural Science Foundation of China[71701089] ; International Innovation Cooperation Project of Jiangsu Province[BZ2020008] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000733532500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/59709]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Cao, Jie |
| 作者单位 | 1.Anhui Polytech Univ, Sch Comp & Informat, Wuhu 241000, Peoples R China 2.Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.Alibaba Grp, Hangzhou 310000, Peoples R China 5.Nanjing Univ Finance & Econ, Jiangsu Prov Key Lab E Business, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Sanmin,Xue, Shan,Wu, Jia,et al. Online Active Learning for Drifting Data Streams[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15. |
| APA | Liu, Sanmin.,Xue, Shan.,Wu, Jia.,Zhou, Chuan.,Yang, Jian.,...&Cao, Jie.(2021).Online Active Learning for Drifting Data Streams.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15. |
| MLA | Liu, Sanmin,et al."Online Active Learning for Drifting Data Streams".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15. |
入库方式： OAI收割
来源：数学与系统科学研究院
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