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.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v63-sahoo56, title = {Cost Sensitive Online Multiple Kernel Classification}, author = {Sahoo, Doyen and Hoi, Steven and Zhao, Peilin}, booktitle = {Proceedings of The 8th Asian Conference on Machine Learning}, pages = {65--80}, year = {2016}, editor = {Durrant, Robert J. and Kim, Kee-Eung}, volume = {63}, series = {Proceedings of Machine Learning Research}, address = {The University of Waikato, Hamilton, New Zealand}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v63/sahoo56.pdf}, url = {https://proceedings.mlr.press/v63/sahoo56.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Cost Sensitive Online Multiple Kernel Classification %A Doyen Sahoo %A Steven Hoi %A Peilin Zhao %B Proceedings of The 8th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Robert J. Durrant %E Kee-Eung Kim %F pmlr-v63-sahoo56 %I PMLR %P 65--80 %U https://proceedings.mlr.press/v63/sahoo56.html %V 63 %X 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.
RIS
TY - CPAPER TI - Cost Sensitive Online Multiple Kernel Classification AU - Doyen Sahoo AU - Steven Hoi AU - Peilin Zhao BT - Proceedings of The 8th Asian Conference on Machine Learning DA - 2016/11/20 ED - Robert J. Durrant ED - Kee-Eung Kim ID - pmlr-v63-sahoo56 PB - PMLR DP - Proceedings of Machine Learning Research VL - 63 SP - 65 EP - 80 L1 - http://proceedings.mlr.press/v63/sahoo56.pdf UR - https://proceedings.mlr.press/v63/sahoo56.html AB - 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. ER -
APA
Sahoo, D., Hoi, S. & Zhao, P.. (2016). Cost Sensitive Online Multiple Kernel Classification. Proceedings of The 8th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 63:65-80 Available from https://proceedings.mlr.press/v63/sahoo56.html.

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