Abstract
The classification performance of the learner is weakened when unlabeled examples are mislabeled during co-training process. A semi-supervised co-training algorithm based on assisted learning (AR-Tri-training) was proposed. Firstly, the assisted learning strategy was presented, which is combined with rich information strategy for designing the assisted learner. Secondly, the evaluation factor was calculated, and noise was eliminated from unlabeled example set by using the assisted learner and the evaluation factor. Finally, three single learners were trained using labeled examples, wrong-learning examples on validation set and less noise unlabeled examples. The experimental results on application to voice recognition indicate that AR-Tri-training can compensate for the Tri-training shortcomings and the average classification accuracy is increased by 15%. As can be drawn from the experimental results, AR-Tri-training not only removes the mislabeled examples in training process, but also takes full advantage of the unlabeled examples and wrong-learning examples on validation set.
This work is supported by Sci. & Tech. Department of Jilin Prov. Grant#20050703-1.
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References
Li, K.-L., Zhang, W., Dai, Y.-N.: Semi-supervised SVM based on Tri-training. Computer Engineering and Applications 45(22), 103–106 (2009) (in Chinese with English abstract)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with Co-training. In: Proceedings 11th Annual Conf. on Computational Learning Theory, Wisconsin USA, pp. 92–100 (1998)
Goldman, S., Zhou, Y.: Enhancing supervised learning with unlabeled data. In: Proceedings 17th Annual Conf. on Machine Learning, California, USA, pp. 327–334 (2000)
Zhou, Z., Li, M.: Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering 17, 1529–1541 (2005)
Nigam, K., Mccallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39, 103–104 (2000)
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings 18th Annual Conf. on Machine Learning, Williamstown, MA, pp. 19–26 (2001)
Deng, C., Guo, M.-Z.: ADE-Tri-training: Tri-training with adaptive data editing. Chinese Journal of Computers 30(8), 1213–1226 (2007) (in Chinese with English abstract)
Bi, H., Liang, H.-L., Wang, J.: Resampling methods and machine learning. Chinese Journal of Computers 32(5), 862–877 (2009) (in Chinese with English abstract)
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© 2011 Springer-Verlag Berlin Heidelberg
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Wang, Hl., Cui, Ry. (2011). Semi-supervised Co-training Algorithm Based on Assisted Learning. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_68
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DOI: https://doi.org/10.1007/978-3-642-23220-6_68
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