中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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
DOI10.1109/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 The policy for discerning drift from noise and replacing outdated instances with new concepts is based on the three criteria of the Ebbinghaus forgetting curve: recall, the fading period, and the memory strength.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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