Cost-Sensitive Online Classification
Conference Proceeding Article
Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of the cost-sensitive measures made by the proposed algorithms, and extensively examine their empirical performance on a variety of cost-sensitive online classification tasks. Finally, we demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be a highly efficient and effective tool to tackle cost-sensitive online classification tasks in various application domains
Computer Sciences | Databases and Information Systems
Data Management and Analytics
IEEE 12th International Conference on Data Mining ICDM 2012: 10-13 December 2012, Brussels, Belgium: Proceedings
City or Country
WANG, Jialei; ZHAO, Peilin; and HOI, Steven C. H..
Cost-Sensitive Online Classification. (2012). IEEE 12th International Conference on Data Mining ICDM 2012: 10-13 December 2012, Brussels, Belgium: Proceedings. 1140-1145. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2346