Cost-sensitive online classification
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/99920 http://hdl.handle.net/10220/13025 |
| _version_ | 1811696252658647040 |
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| author | Hoi, Steven C. H. Wang, Jialei. Zhao, Peilin. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Hoi, Steven C. H. Wang, Jialei. Zhao, Peilin. |
| author_sort | Hoi, Steven C. H. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:36:25Z |
| format | Conference Paper |
| id | ntu-10356/99920 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:36:25Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/999202020-05-28T07:17:26Z Cost-sensitive online classification Hoi, Steven C. H. Wang, Jialei. Zhao, Peilin. School of Computer Engineering IEEE International Conference on Data Mining (12th : 2012 : Brussels, Belgium) DRNTU::Engineering::Computer science and engineering 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. 2013-08-06T02:57:17Z 2019-12-06T20:13:38Z 2013-08-06T02:57:17Z 2019-12-06T20:13:38Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99920 http://hdl.handle.net/10220/13025 10.1109/ICDM.2012.116 en |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Hoi, Steven C. H. Wang, Jialei. Zhao, Peilin. Cost-sensitive online classification |
| title | Cost-sensitive online classification |
| title_full | Cost-sensitive online classification |
| title_fullStr | Cost-sensitive online classification |
| title_full_unstemmed | Cost-sensitive online classification |
| title_short | Cost-sensitive online classification |
| title_sort | cost sensitive online classification |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/99920 http://hdl.handle.net/10220/13025 |
| work_keys_str_mv | AT hoistevench costsensitiveonlineclassification AT wangjialei costsensitiveonlineclassification AT zhaopeilin costsensitiveonlineclassification |