Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2019

Abstract

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, for better trade off between the performance and efficiency, we further introduce the sketching technique into our algorithms, which significantly accelerates the computational speed with quite slight performance loss. Finally, we apply our algorithms to tackle several online anomaly detection tasks from real world. Promising results prove that the proposed algorithms are effective and efficient in solving cost-sensitive online classification problems in various real-world domains.

Keywords

Adaptive Regularization, Cost-Sensitive Classification, Online Learning, Sketching Learning

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

31

Issue

2

First Page

214

Last Page

228

ISSN

1041-4347

Identifier

10.1109/TKDE.2018.2826011

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2018.2826011

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