Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 31, Issue 2, Pages 214-228Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2826011
Keywords
Cost-sensitive classification; online learning; adaptive regularization; sketching learning
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Funding
- National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative
- National Natural Science Foundation of China (NSFC) [61602185]
- Fundamental Research Funds for the Central Universities [D2172480]
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Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity and (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, for better trade off between the performance and efficiency, we further introduce the sketching technique into our algorithms, which significantly accelerates the computational speed with quite slight performance loss. Finally, we apply our algorithms to tackle several online anomaly detection tasks from real world. Promising results prove that the proposed algorithms are effective and efficient in solving cost-sensitive online classification problems in various real-world domains.
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