Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

11-2015

Abstract

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.

Keywords

Cost-Sensitive Classification, Online Learning, Adaptive Regularization

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

IEEE International Conference on Data Mining ICDM 2015: 14-17 November 2015, Atlantic City, NJ: Proceedings

First Page

649

Last Page

658

ISBN

9781467395038

Identifier

10.1109/ICDM.2015.51

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/ICDM.2015.51

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