Cost-Sensitive Online Classification
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 Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers (IEEE)
WANG, Jialei; ZHAO, Peilin; and HOI, Steven C. H..
Cost-Sensitive Online Classification. (2014). IEEE Transactions on Knowledge and Data Engineering. 26, (10), 2425-2438. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2921