Cost-Sensitive Double Updating Online Learning and Its Application to Online Anomaly Detection
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 optimiz...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2013
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/2338 https://ink.library.smu.edu.sg/context/sis_research/article/3338/viewcontent/Cost_Sensitive_Double_Updating_Online_Learning_and_Its_Application_to_Online_Anomaly_Detection.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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