Cost sensitive online multiple kernel classification
Learning from data streams has been an important open research problem in the era of big data analytics. This paper investigates supervised machine learning techniques for mining data streams with application to online anomaly detection. Unlike conventional machine learning tasks, machine learning f...
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sg-smu-ink.sis_research-44432020-03-24T03:43:31Z Cost sensitive online multiple kernel classification SAHOO, Doyen ZHAO, Peilin HOI, Steven C. H. Learning from data streams has been an important open research problem in the era of big data analytics. This paper investigates supervised machine learning techniques for mining data streams with application to online anomaly detection. Unlike conventional machine learning tasks, machine learning from data streams for online anomaly detection has several challenges: (i) data arriving sequentially and increasing rapidly, (ii) highly class-imbalanced distributions; and (iii) complex anomaly patterns that could evolve dynamically.To tackle these challenges, we propose a novel Cost-Sensitive Online Multiple Kernel Classification (CSOMKC) scheme for comprehensively mining data streams and demonstrate its application to online anomaly detection. Specifically, CSOMKC learns a kernel-based cost-sensitive prediction model for imbalanced data streams in a sequential or online learning fashion, in which a pool of multiple diverse kernels is dynamically explored.The optimal kernel predictor and the multiple kernel combination are learnt together, and simultaneously class imbalance issues are addressed. We give both theoretical and extensive empirical analysis of the proposed algorithms. 2016-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3442 https://ink.library.smu.edu.sg/context/sis_research/article/4443/viewcontent/Cost_sensitive_online_multiple_kernel_classification.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 Cost-Sensitive Learning Online Learning Multiple Kernel Learning Databases and Information Systems |
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Cost-Sensitive Learning Online Learning Multiple Kernel Learning Databases and Information Systems SAHOO, Doyen ZHAO, Peilin HOI, Steven C. H. Cost sensitive online multiple kernel classification |
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Learning from data streams has been an important open research problem in the era of big data analytics. This paper investigates supervised machine learning techniques for mining data streams with application to online anomaly detection. Unlike conventional machine learning tasks, machine learning from data streams for online anomaly detection has several challenges: (i) data arriving sequentially and increasing rapidly, (ii) highly class-imbalanced distributions; and (iii) complex anomaly patterns that could evolve dynamically.To tackle these challenges, we propose a novel Cost-Sensitive Online Multiple Kernel Classification (CSOMKC) scheme for comprehensively mining data streams and demonstrate its application to online anomaly detection. Specifically, CSOMKC learns a kernel-based cost-sensitive prediction model for imbalanced data streams in a sequential or online learning fashion, in which a pool of multiple diverse kernels is dynamically explored.The optimal kernel predictor and the multiple kernel combination are learnt together, and simultaneously class imbalance issues are addressed. We give both theoretical and extensive empirical analysis of the proposed algorithms. |
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SAHOO, Doyen ZHAO, Peilin HOI, Steven C. H. |
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SAHOO, Doyen ZHAO, Peilin HOI, Steven C. H. |
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SAHOO, Doyen |
title |
Cost sensitive online multiple kernel classification |
title_short |
Cost sensitive online multiple kernel classification |
title_full |
Cost sensitive online multiple kernel classification |
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Cost sensitive online multiple kernel classification |
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Cost sensitive online multiple kernel classification |
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cost sensitive online multiple kernel classification |
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Institutional Knowledge at Singapore Management University |
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2016 |
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https://ink.library.smu.edu.sg/sis_research/3442 https://ink.library.smu.edu.sg/context/sis_research/article/4443/viewcontent/Cost_sensitive_online_multiple_kernel_classification.pdf |
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