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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Boyang Li Liangpeng Ma Jinglu Hu Hirasawa, K. |
| Copyright Year | 2008 |
| Description | Author affiliation: Grad. Sch. of Inf., Waseda Univ., Kitakyushu (Boyang Li; Liangpeng Ma; Jinglu Hu; Hirasawa, K.) |
| Abstract | One of the central problems of functional genomics is gene classification. Microarray data are currently a major source of information about the functionality of genes. Various mathematical techniques, such as neural networks (NNs), self-organizing map (SOM) and several statistical methods, have been applied to classify the data in attempts to extract the underlying knowledge. As for conventional classification, the problem mainly addressed so far has been how to classify the multi-label gene data and how to deal with the imbalance problem. In this paper, we proposed an improved support vector machine (SVM) classifier with soft decision boundary. This boundary is a classification boundary based on belief degrees of data. The boundary can reflect the distribution of data, especially in the mutual part between classes and the excursion caused by the data imbalance. |
| Starting Page | 2476 |
| Ending Page | 2480 |
| File Size | 663670 |
| Page Count | 5 |
| File Format | |
| ISBN | 9784907764302 |
| DOI | 10.1109/SICE.2008.4655081 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-08-20 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | SICE |
| Subject Keyword | Soft Decision-making Boundary SVM Gene Classification Multi-label Gene Data Data Imbalance |
| Content Type | Text |
| Resource Type | Article |
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