Conference Proceeding Article
Cost-Sensitive Online Classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies have proved that incorporating second order information could yield significant improvements on the prediction model. Hence, we propose a novel cost-sensitive online classification algorithm with adaptive regularization. We theoretically analyzed the proposed algorithm and empirically validated its effectiveness with extensive experiments. We also demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be an effective tool to tackle cost-sensitive online classification tasks in various application domains.
Cost-Sensitive Classification, Online Learning, Adaptive Regularization
Databases and Information Systems
Data Management and Analytics
IEEE International Conference on Data Mining ICDM 2015: 14-17 November 2015, Atlantic City, NJ: Proceedings
IEEE Computer Society
City or Country
Los Alamitos, CA
ZHAO, Peilin; ZHUANG, Furen; WU, Min; LI, Xiao-Li; and HOI, Steven C. H..
Cost-sensitive online classification with adaptive regularization and its applications. (2015). IEEE International Conference on Data Mining ICDM 2015: 14-17 November 2015, Atlantic City, NJ: Proceedings. 649-658. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2923
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