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Frontiers of Computer Science

, Volume 13, Issue 1, pp 99–105 | Cite as

Towards making co-training suffer less from insufficient views

  • Xiangyu Guo
  • Wei WangEmail author
Research Article
  • 9 Downloads

Abstract

Co-training is a famous semi-supervised learning algorithm which can exploit unlabeled data to improve learning performance. Generally it works under a two-view setting (the input examples have two disjoint feature sets in nature), with the assumption that each view is sufficient to predict the label. However, in real-world applications due to feature corruption or feature noise, both views may be insufficient and co-training will suffer from these insufficient views. In this paper, we propose a novel algorithm named Weighted Co-training to deal with this problem. It identifies the newly labeled examples that are probably harmful for the other view, and decreases their weights in the training set to avoid the risk. The experimental results show that Weighted Co-training performs better than the state-of-art co-training algorithms on several benchmarks.

Keywords

semi-supervised learning co-training insufficient views 

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Notes

Acknowledgements

This work was supported by the NSFC (61673202, 61305067), the Fundamental Research Funds for the Central Universities, and the Collaborative Innovation Center of Novel Software Technology and Industrialization.

Supplementary material

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References

  1. 1.
    Miller D J, Uyar H S. A mixture of experts classifier with learning based on both labelled and unlabelled data. Advances in Neural Information Processing Systems, 1997, 571–577Google Scholar
  2. 2.
    Nigam K, McCallum A, Thrun S, Mitchell T. Text classification from labeled and unlabeled documents using EM. Machine Learning, 2000, 39(2/3): 103–134CrossRefzbMATHGoogle Scholar
  3. 3.
    Bennett K P, Demiriz A. Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 1998, 368–374Google Scholar
  4. 4.
    Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning. 1999, 200–209Google Scholar
  5. 5.
    Blum A, Chawla S. Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the 18th International Conference on Machine Learning. 2001, 19–26Google Scholar
  6. 6.
    Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning. 2003, 912–919Google Scholar
  7. 7.
    Zhou D, Bousquet O, Lal T N, Weston J, Schölkopf B. Learning with local and global consistency. Advances in Neural Information Processing Systems, 2003, 321–328Google Scholar
  8. 8.
    Blum A, Mitchell T. Combining labeled and unlabeled data with cotraining. In: Proceedings of the 11th Annual Conference on Computational Learning Theory. 1998, 92–100Google Scholar
  9. 9.
    Zhou Z H, Li M. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529–1541CrossRefGoogle Scholar
  10. 10.
    Zhou Z H, Li M. Semi-supervised learning by disagreement. Knowledge and Information System, 2010, 24(3): 415–439MathSciNetCrossRefGoogle Scholar
  11. 11.
    Nigam K, Ghani R. Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 10th International Conference on Information and Knowledge Management. 2000, 86–93Google Scholar
  12. 12.
    Goldman S A, Zhou Y. Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th International Conference on Machine Learning. 2000, 327–334Google Scholar
  13. 13.
    Kiritchenko S, Matwin S. Email classification with co-training. In: Proceedings of the 2001 Conference of the Centre for Advanced Studies on Collaborative Research. 2001, 301–312Google Scholar
  14. 14.
    Maeireizo B, Litman D, Hwa R. Co-training for predicting emotions with spoken dialogue data. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions. 2004, 28CrossRefGoogle Scholar
  15. 15.
    Wan X. Co-training for cross-lingual sentiment classification. In: Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009, 235–243Google Scholar
  16. 16.
    Liu R, Cheng J, Lu H. A robust boosting tracker with minimum error bound in a co-training framework. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 1459–1466Google Scholar
  17. 17.
    Abney S P. Bootstrapping. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 2002, 360–367Google Scholar
  18. 18.
    Balcan M F, Blum A, Yang K. Co-training and expansion: towards bridging theory and practice. Advances in Neural Information Processing Systems, 2004, 89–96Google Scholar
  19. 19.
    Wang W, Zhou Z H. A new analysis of co-training. In: Proceedings of the 27th International Conference on Machine Learning. 2010, 1135–1142Google Scholar
  20. 20.
    Wang W, Zhou Z H. Analyzing co-training style algorithms. In: Proceedings of the 18th European Conference on Machine Learning. 2007, 454–465Google Scholar
  21. 21.
    Wang W, Zhou Z H. Co-training with insufficient views. In: Proceedings of the 5th Asian Conference on Machine Learning. 2013, 467–482Google Scholar
  22. 22.
    Xu J, He H, Man H. DCPE co-training for classification. Neurocomputing, 2012, 86: 75–85CrossRefGoogle Scholar
  23. 23.
    Kushmerick N. Learning to remove internet advertisements. In: Proceedings of the 3rd Annual Conference on Autonomous Agents. 1999, 175–181CrossRefGoogle Scholar
  24. 24.
    Giles C L, Bollacker K D, Lawrence S. Citeseer: an automatic citation indexing system. In: Proceedings of the 3rd ACM International Conference on Digital Libraries. 1998, 89–98CrossRefGoogle Scholar
  25. 25.
    Bisson G, Grimal C. Co-clustering of multi-view datasets: a parallelizable approach. In: Proceedings of the 12th IEEE International Conference on Data Mining. 2012, 828–833Google Scholar
  26. 26.
    Lichman M. UCI machine learning repository. 2013Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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