Conference Proceeding Article
Learning from data streams has been an important open research problem in the era ofbig data analytics. This paper investigates supervised machine learning techniques formining data streams with application to online anomaly detection. Unlike conventionalmachine learning tasks, machine learning from data streams for online anomaly detectionhas several challenges: (i) data arriving sequentially and increasing rapidly, (ii) highlyclass-imbalanced distributions; and (iii) complex anomaly patterns that could evolve dynamically.To tackle these challenges, we propose a novel Cost-Sensitive Online MultipleKernel Classification (CSOMKC) scheme for comprehensively mining data streams anddemonstrate its application to online anomaly detection. Specifically, CSOMKC learns akernel-based cost-sensitive prediction model for imbalanced data streams in a sequential oronline 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, andsimultaneously class imbalance issues are addressed. We give both theoretical and extensiveempirical analysis of the proposed algorithms.
Cost-Sensitive Learning, Online Learning, Multiple Kernel Learning
Categorical Data Analysis | Databases and Information Systems
Data Management and Analytics
Volume 63: Proceedings of The 8th Asian Conference on Machine Learning: Hamilton, New Zealand, 2016 November 16-18
City or Country
SAHOO, Doyen; ZHAO, Peilin; and HOI, Steven C. H..
Cost sensitive online multiple kernel classification. (2016). Volume 63: Proceedings of The 8th Asian Conference on Machine Learning: Hamilton, New Zealand, 2016 November 16-18. 65-80. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3442
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