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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hoyoung Woo Cheong Hee Park |
| Copyright Year | 2012 |
| Abstract | Ensemble learning has been widely used in data mining and pattern recognition. However, when the number of labeled data samples is very small, it is difficult to train a base classifier for ensemble learning, therefore, it is necessary to utilize an abundance of unlabeled data effectively. In most semi-supervised ensemble methods, the label prediction of unlabeled data and their use as pseudo-label data are common processes. However, the low accuracy of the label prediction of unlabeled data limits the ability to obtain improved ensemble members. In this paper, we propose effective ensemble learning methods for semi-supervised classification that combine label propagation and ensemble learning. We show that accurate ensemble members can be constructed using class labels predicted by a label propagation method, and unlabeled data samples are fully utilized for diverse ensemble member construction. Extensive experimental results demonstrate the performance of the proposed methods. |
| Starting Page | 421 |
| Ending Page | 426 |
| File Size | 428037 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467348737 |
| DOI | 10.1109/CIT.2012.98 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Accuracy Semi-supervised learning Semisupervised learning Boosting Data mining Ensemble learning Label propagation Bagging |
| Content Type | Text |
| Resource Type | Article |
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