| 用于co-training的特征选择技术 |
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| 引用本文： | 李国正,刘天羽.用于co-training的特征选择技术[J].上海大学学报(英文版),2008,12(1):47-51. |
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| 作者姓名： | 李国正 刘天羽 |
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| 作者单位： | [1]School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P. R. China [2]School of Electronic, Shanghai Dianji University, Shanghai 200240, P. R. China |
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| 摘 要： | Co-training is a semi-supervised learning method, which employs two complementary learners to label the unlabeled data for each other and to predict the test sample together. Previous studies show that redundant information can help improve the ratio of prediction accuracy between semi-supervised learning methods and supervised learning methods. However, redundant information often practically hurts the performance of learning machines. This paper investigates what redundant features have effect on the semi-supervised learning methods, e.g. co-training, and how to remove the redundant features as well as the irrelevant features. Here, FESCOT （feature selection for co-training） is proposed to improve the generalization performance of co-training with feature selection. Experimental results on artificial and real world data sets show that FESCOT helps to remove irrelevant and redundant features that hurt the performance of the co-training method.
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| 关 键 词： | CO—training 特征 选择技术 电子技术 |
| 文章编号： | 10.1007/s11741-008-0110-2 |
| 收稿时间： | 2006-05-31 |
| 修稿时间： | 2006-07-17 |
Feature selection for co-training |
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| Guo-zheng Li,Tian-yu Liu.Feature selection for co-training[J].Journal of Shanghai University(English Edition),2008,12(1):47-51. |
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| Authors: | Guo-zheng Li Tian-yu Liu |
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| Institution: | 1. School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P. R. China
2. School of Electronic, Shanghai Dianji University, Shanghai 200240, P. R. China |
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| Abstract: | Co-training is a semi-supervised learning method, which employs two complementary learners to label the unlabeleddata for each other and to predict the test sample together. Previous studies show that redundant information can helpimprove the ratio of prediction accuracy between semi-supervised learning methods and supervised learning methods. However,redundant information often practically hurts the performance of learning machines. This paper investigates what redundantfeatures have effect on the semi-supervised learning methods, e.g. co-training, and how to remove the redundant features aswell as the irrelevant features. Here, FESCOT (feature selection for co-training) is proposed to improve the generalizationperformance of co-training with feature selection. Experimental results on artificial and real world data sets show that FESCOThelps to remove irrelevant and redundant features that hurt the performance of the co-training method. |
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| Keywords: | feature selection semi-supervised learning co-training |
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