High-dimensional Data Stream Classification via Sparse Online Learning

The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve...

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Main Authors: WANG, Dayong, WU, Pengcheng, ZHAO, Peilin, WU, Yue, MIAO, Chunyan, HOI, Steven C. H.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2646
https://ink.library.smu.edu.sg/context/sis_research/article/3646/viewcontent/High_d_data_stream_SO_2014_ICDM_afv.pdf
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spelling sg-smu-ink.sis_research-36462018-12-03T00:41:29Z High-dimensional Data Stream Classification via Sparse Online Learning WANG, Dayong WU, Pengcheng ZHAO, Peilin WU, Yue MIAO, Chunyan HOI, Steven C. H. The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification. We conduct an extensive set of experiments, in which encouraging results validate the efficacy of the proposed algorithms in comparison to a family of state-of-the-art techniques on a variety of data stream classification tasks. 2014-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2646 info:doi/10.1109/ICDM.2014.46 https://ink.library.smu.edu.sg/context/sis_research/article/3646/viewcontent/High_d_data_stream_SO_2014_ICDM_afv.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 data stream classification online learning sparse Databases and Information Systems 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 data stream classification
online learning
sparse
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle data stream classification
online learning
sparse
Databases and Information Systems
Numerical Analysis and Scientific Computing
WANG, Dayong
WU, Pengcheng
ZHAO, Peilin
WU, Yue
MIAO, Chunyan
HOI, Steven C. H.
High-dimensional Data Stream Classification via Sparse Online Learning
description The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification. We conduct an extensive set of experiments, in which encouraging results validate the efficacy of the proposed algorithms in comparison to a family of state-of-the-art techniques on a variety of data stream classification tasks.
format text
author WANG, Dayong
WU, Pengcheng
ZHAO, Peilin
WU, Yue
MIAO, Chunyan
HOI, Steven C. H.
author_facet WANG, Dayong
WU, Pengcheng
ZHAO, Peilin
WU, Yue
MIAO, Chunyan
HOI, Steven C. H.
author_sort WANG, Dayong
title High-dimensional Data Stream Classification via Sparse Online Learning
title_short High-dimensional Data Stream Classification via Sparse Online Learning
title_full High-dimensional Data Stream Classification via Sparse Online Learning
title_fullStr High-dimensional Data Stream Classification via Sparse Online Learning
title_full_unstemmed High-dimensional Data Stream Classification via Sparse Online Learning
title_sort high-dimensional data stream classification via sparse online learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2646
https://ink.library.smu.edu.sg/context/sis_research/article/3646/viewcontent/High_d_data_stream_SO_2014_ICDM_afv.pdf
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