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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xi-Zhao Wang Jian-Hui Yan Ran Wang Chun-Ru Dong |
| Copyright Year | 2007 |
| Description | Author affiliation: Hebei Univ., Baoding (Xi-Zhao Wang; Jian-Hui Yan) |
| Abstract | The generalization capability of a classifier will probably be degenerated when the classifier is generated from a dataset containing redundancy. To remove the redundancy, sample selection methods which choose the most valuable and representative instances from the original date set, can be used to obtain a subset of the original dataset. It is expected that the classifier trained from the subset can achieve no lower generalization capability than the classifier trained from the original data set. This paper proposes a sample selection method based on maximum entropy of testing instances in the fuzzy decision tree induction, and also gives the related theoretical analyses. |
| Starting Page | 3621 |
| Ending Page | 3626 |
| File Size | 442177 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424409907 |
| DOI | 10.1109/ICSMC.2007.4413726 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-07 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Decision trees Algorithm design and analysis Machine learning algorithms Testing Iterative algorithms Learning systems Machine learning Entropy Classification tree analysis Cellular neural networks |
| Content Type | Text |
| Resource Type | Article |
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