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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dengyuan Wu Kai Wang Tao He Jicheng Ren |
| Copyright Year | 2008 |
| Description | Author affiliation: Zhongke Fulong Comput. Technol. Co., Beijing (Tao He; Jicheng Ren) || Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing (Dengyuan Wu; Kai Wang) |
| Abstract | In the real world concepts are not stable and change with time and a lot of other hidden factors. Stream classifiers should be sensitive to the drifting of concept in an automatic way. In this paper, we proposed a new weighted majority strategy for the ensemble classifier. We periodically created and evaluated component classifiers that constitute the ensemble then we used the weighted ensemble to make global prediction. We empirically evaluated two kinds of concept drifting: the SEA concept drifting and the moving hyper-plane problem. Experiment results showed that our proposed method was very effective to deal with concept drifting. |
| Starting Page | 1854 |
| Ending Page | 1859 |
| File Size | 351174 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769533988 |
| DOI | 10.1109/ICYCS.2008.491 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | ensemble learning Weather forecasting Economic forecasting concept drifting Data mining History Helium Computer buffers stream classification |
| Content Type | Text |
| Resource Type | Article |
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