Cost Sensitive Online Multiple Kernel Classification


Doyen Sahoo, Steven Hoi, Peilin Zhao ;
Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:65-80, 2016.


Mining 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 data mining tasks, mining 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 Cost-Sensitive Online Multiple Kernel Classification (CSOMKC) 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 perform theoretical and extensive empirical analysis of the proposed algorithms.

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