A unified framework for sparse online learning

The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalabili...

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Main Authors: ZHAO, Peilin, WONG, Dayong, WU, Pengcheng, HOI, Steven C. H.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5957
https://ink.library.smu.edu.sg/context/sis_research/article/6960/viewcontent/UnitedFrameworkSOL_2020_av.pdf
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spelling sg-smu-ink.sis_research-69602021-05-24T06:48:11Z A unified framework for sparse online learning ZHAO, Peilin WONG, Dayong WU, Pengcheng HOI, Steven C. H. The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online learning algorithm, which could successfully handle online anomaly detection tasks with the extremely unbalanced class distribution. As the performance evaluation, we analyze the theoretical bounds of the proposed algorithms and conduct an extensive set of experiments. The encouraging experimental results demonstrate the efficacy of the proposed algorithms over the state-of-the-art techniques on multiple data stream classification tasks. 2020-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5957 info:doi/10.1145/3361559 https://ink.library.smu.edu.sg/context/sis_research/article/6960/viewcontent/UnitedFrameworkSOL_2020_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Online learning sparse learning classification cost-sensitive learning Databases and Information Systems Data Science Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Online learning
sparse learning
classification
cost-sensitive learning
Databases and Information Systems
Data Science
Numerical Analysis and Scientific Computing
spellingShingle Online learning
sparse learning
classification
cost-sensitive learning
Databases and Information Systems
Data Science
Numerical Analysis and Scientific Computing
ZHAO, Peilin
WONG, Dayong
WU, Pengcheng
HOI, Steven C. H.
A unified framework for sparse online learning
description The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online learning algorithm, which could successfully handle online anomaly detection tasks with the extremely unbalanced class distribution. As the performance evaluation, we analyze the theoretical bounds of the proposed algorithms and conduct an extensive set of experiments. The encouraging experimental results demonstrate the efficacy of the proposed algorithms over the state-of-the-art techniques on multiple data stream classification tasks.
format text
author ZHAO, Peilin
WONG, Dayong
WU, Pengcheng
HOI, Steven C. H.
author_facet ZHAO, Peilin
WONG, Dayong
WU, Pengcheng
HOI, Steven C. H.
author_sort ZHAO, Peilin
title A unified framework for sparse online learning
title_short A unified framework for sparse online learning
title_full A unified framework for sparse online learning
title_fullStr A unified framework for sparse online learning
title_full_unstemmed A unified framework for sparse online learning
title_sort unified framework for sparse online learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5957
https://ink.library.smu.edu.sg/context/sis_research/article/6960/viewcontent/UnitedFrameworkSOL_2020_av.pdf
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