DocumentCode :
1251790
Title :
CoTrade: Confident Co-Training With Data Editing
Author :
Zhang, Min-Ling ; Zhou, Zhi-Hua
Author_Institution :
Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China
Volume :
41
Issue :
6
fYear :
2011
Firstpage :
1612
Lastpage :
1626
Abstract :
Co-training is one of the major semi-supervised learning paradigms that iteratively trains two classifiers on two different views, and uses the predictions of either classifier on the unlabeled examples to augment the training set of the other. During the co-training process, especially in initial rounds when the classifiers have only mediocre accuracy, it is quite possible that one classifier will receive labels on unlabeled examples erroneously predicted by the other classifier. Therefore, the performance of co-training style algorithms is usually unstable. In this paper, the problem of how to reliably communicate labeling information between different views is addressed by a novel co-training algorithm named COTRADE. In each labeling round, COTRADE carries out the label communication process in two steps. First, confidence of either classifier´s predictions on unlabeled examples is explicitly estimated based on specific data editing techniques. Secondly, a number of predicted labels with higher confidence of either classifier are passed to the other one, where certain constraints are imposed to avoid introducing undesirable classification noise. Experiments on several real-world datasets across three domains show that COTRADE can effectively exploit unlabeled data to achieve better generalization performance.
Keywords :
data handling; learning (artificial intelligence); pattern classification; COTRADE; classifier training; confident cotraining; data editing; semisupervised learning; Algorithm design and analysis; Labeling; Machine learning; Prediction algorithms; Semisupervised learning; Bias-variance decomposition; co-training; data editing; machine learning; semi-supervised learning;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2011.2157998
Filename :
5910412
Link To Document :
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