Conference Proceeding Article
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.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Proceedings of the 2013 SIAM International Conference on Data Mining: May 2-4, Austin Texas
City or Country
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. Research Collection School Of Information Systems.
Available at: http://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 License.