Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2013
Abstract
Although both cost-sensitive classification and online learning have been well studied separately in data mining and machine learning, there was very few comprehensive study of cost-sensitive online classification in literature. In this paper, we formally investigate this problem by directly optimizing cost-sensitive measures for an online classification task. As the first comprehensive study, we propose the Cost-Sensitive Double Updating Online Learning (CSDUOL) algorithms, which explores a recent double updating technique to tackle the online optimization task of cost-sensitive classification by maximizing the weighted sum or minimizing the weighted misclassification cost. We theoretically analyze the cost-sensitive measure bounds of the proposed algorithms, extensively examine their empirical performance for cost-sensitive online classification tasks, and finally demonstrate the application of our technique to solve online anomaly detection tasks.
Discipline
Computer Sciences | Databases and Information Systems
Publication
Proceedings of the 2013 SIAM International Conference on Data Mining: May 2-4, Austin Texas
First Page
207
Last Page
215
ISBN
9781611972627
Identifier
10.1137/1.9781611972832.23
Publisher
SIAM
City or Country
Philadelphia, PA
Citation
ZHAO, Peilin and HOI, Steven C. H..
Cost-Sensitive Double Updating Online Learning and Its Application to Online Anomaly Detection. (2013). Proceedings of the 2013 SIAM International Conference on Data Mining: May 2-4, Austin Texas. 207-215.
Available at: https://ink.library.smu.edu.sg/sis_research/2338
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1137/1.9781611972832.23
