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https://hdl.handle.net/10356/99920
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hoi, Steven C. H. | en |
dc.contributor.author | Wang, Jialei. | en |
dc.contributor.author | Zhao, Peilin. | en |
dc.date.accessioned | 2013-08-06T02:57:17Z | en |
dc.date.accessioned | 2019-12-06T20:13:38Z | - |
dc.date.available | 2013-08-06T02:57:17Z | en |
dc.date.available | 2019-12-06T20:13:38Z | - |
dc.date.copyright | 2012 | en |
dc.date.issued | 2012 | en |
dc.identifier.uri | https://hdl.handle.net/10356/99920 | - |
dc.description.abstract | Both cost-sensitive classification and online learning have been studied extensively in data mining and machine learning communities, respectively. It is a bit surprising that there was very limited comprehensive study for addressing 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 exploiting the idea of online gradient descent techniques. Based on the framework, we propose a family of 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. | en |
dc.language.iso | en | en |
dc.subject | DRNTU::Engineering::Computer science and engineering | en |
dc.title | Cost-sensitive online classification | en |
dc.type | Conference Paper | en |
dc.contributor.school | School of Computer Engineering | en |
dc.contributor.conference | IEEE International Conference on Data Mining (12th : 2012 : Brussels, Belgium) | en |
dc.identifier.doi | 10.1109/ICDM.2012.116 | en |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
Appears in Collections: | SCSE Conference Papers |
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