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Online Active Learning for Drifting Data Streams
Liu, Sanmin1,2; Xue, Shan2; Wu, Jia2; Zhou, Chuan3; Yang, Jian2; Li, Zhao4; Cao, Jie5
AbstractClassification 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 The policy for discerning drift from noise and replacing outdated instances with new concepts is based on the three criteria of the Ebbinghaus forgetting curve: recall, the fading period, and the memory strength.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.
KeywordLabeling Data models Uncertainty Biological system modeling Computational modeling Cognition Adaptation models Active learning concept drift data stream classification online incremental learning
Indexed BySCI
Funding ProjectNature 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000733532500001
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Document Type期刊论文
Corresponding AuthorCao, Jie
Affiliation1.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
Recommended Citation
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.
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