RESEARCH ARTICLE

When semi-supervised learning meets ensemble learning

  • Zhi-Hua Zhou
Expand
  • National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China

Received date: 12 Jul 2010

Accepted date: 18 Oct 2010

Published date: 05 Mar 2011

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

Semi-supervised learning and ensemble learning are two important machine learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. Although both paradigms have achieved great success during the past decade, they were almost developed separately. In this paper, we advocate that semi-supervised learning and ensemble learning are indeed beneficial to each other, and stronger learning machines can be generated by leveraging unlabeled data and classifier combination.

Cite this article

Zhi-Hua Zhou . When semi-supervised learning meets ensemble learning[J]. Frontiers of Electrical and Electronic Engineering, 0 , 6(1) : 6 -16 . DOI: 10.1007/s11460-011-0126-2

1
Chapelle O, Schölkopf B, Zien A. Semi-Supervised Learning. Cambridge: MIT Press, 2006

2
Zhou Z H, Li M. Semi-supervised learning by disagreement. Knowledge and Information Systems, 2010, 24(3): 415-439

DOI

3
Zhu X. Semi-supervised learning literature survey. Technical Report 1530. Madison: University of Wisconsin at Madison, Department of Computer Sciences, 2006. http://www.cs.wisc.edu/∼jerryzhu/pub/ssl survey.pdf

4
Zhou Z H. Ensemble Learning. In: Li S Z, ed. Encyclopedia of Biometrics. Berlin: Springer, 2009, 270-273

5
Bennett K, Demiriz A, Maclin R. Exploiting unlabeled data in ensemble methods. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2002, 289-296

6
d’Alché-Buc F, Grandvalet Y, Ambroise C. Semi-Supervised MarginBoost. In: Dietterich T G, Becker S, Ghahramani Z, eds. Advances in Neural Information Processing Systems 14. 2002, 553-560

7
Li M, Zhou Z H. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 2007, 37(6): 1088-1098

DOI

8
Mallapragada P K, Jin R, Jain A K, Liu Y. SemiBoost: boosting for semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2000-2014

DOI

9
Valizadegan H, Jin R, Jain A K. Semi-supervised boosting for multi-class classification. In: Proceedings of the 19th European Conference on Machine Learning. 2008, 522-537

10
Zhou Z H, Li M. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541

DOI

11
Zhou Z H. When semi-supervised learning meets ensemble learning. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems. 2010, 529-538

12
Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning. In: Tesauro G, Touretzky D S, Leen T K, eds. Advances in Neural Information Processing Systems 7. Cambridge: MIT Press, 1995, 231-238

13
Brown G. An information theoretic perspective on multiple classifier systems. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems. 2009, 344-353

DOI

14
Zhou Z H, Li N. Multi-information ensemble diversity. In: Proceedings of the 9th International Workshop on Multiple Classifier Systems. 2010, 134-144

DOI

15
Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123-140

DOI

16
Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1997, 55(1): 119-139

DOI

17
Ho T K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844

DOI

18
Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32

DOI

19
Zhou Z H. Learning with unlabeled data and its application to image retrieval. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence. 2006, 5-10

20
Vapnik V N. Statistical Learning Theory. New York: Wiley, 1998

21
Settles B. Active learning literature survey. Technical Report 1648. Wisconsin: University of Wisconsin at Madison, Department of Computer Sciences, 2009. http://pages.cs.wisc.edu/∼bsettles/pub/settles.activelearning.pdf

22
Miller D J, Uyar H S. A mixture of experts classifier with learning based on both labelled and unlabelled data. In: Mozer M, Jordan M I, Petsche T, eds. Advances in Neural Information Processing Systems 9. Cambridge: MIT Press, 1997, 571-577

23
Nigam K, McCallum A K, Thrun S, Mitchell T. Text classification from labeled and unlabeled documents using EM. Machine Learning, 2000, 39(2-3): 103-134

DOI

24
Shahshahani B, Landgrebe D. The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1087-1095

DOI

25
Xu L. Bayesian Ying Yang System and Theory as a Unified Statistical Learning Approach: (I) Unsupervised and Semi-Unsupervised Learning. In: Amari S, Kassabov N, eds. Brain-like Computing and Intelligent Information Systems. Berlin: Springer-Verlag, 1997, 241-274

26
Chapelle O, Zien A. Semi-supervised learning by low density separation. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005, 57-64

27
Grandvalet Y, Bengio Y. Semi-supervised Learning by Entropy Minimization. In: Saul L K, Weiss Y, Bottou L, eds. Advances in Neural Information Processing Systems 17. Cambridge: MIT Press, 2005, 529-536

28
Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning. 1999, 200-209

29
Lawrence N D, Jordan M I. Semi-supervised learning via Gaussian processes. In: Saul L K, Weiss Y, Bottou L, eds. Advances in Neural Information Processing Systems 17. Cambridge: MIT Press, 2005, 753-760

30
Belkin M, Niyogi P. Semi-supervised learning on Riemannian manifolds. Machine Learning, 2004, 56(1-3): 209-239

DOI

31
Belkin M, Niyogi P, Sindhwani V. On manifold regularization. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005, 17-24

32
Belkin M, Niyogi P, Sindhwani V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2006, 7: 2399-2434

33
Zhou D, Bousquet O, Lal T N, J. Weston, Schölkopf B. Learning with local and global consistency. In: Thrun S, Saul L, Schölkopf B, eds. Advances in Neural Information Processing Systems 16. 2004, 321-328

34
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, 912-919

35
Blum A, Mitchell T. Combining labeled and unlabeleddata with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory. 1998, 92-100

36
Dasgupta S, Littman M, McAllester D. PAC Generalization Bunds for Co-training. In: Dietterich T G, Becker S, Ghahramani Z, eds. Advances in Neural Information Processing Systems 14. Cambridge: MIT Press, 2002, 375-382

37
Balcan M F, Blum A, Yang K. Co-Training and Expansion: Towards Bridging Theory and Practice. In: Saul L K, Weiss Y, Bottou L, eds. Advances in Neural Information Processing Systems 17. Cambridge: MIT Press, 2005, 89-96

38
Abney S. Bootstrapping. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 2002, 360-367

39
Nigam K, Ghani R. Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th ACM International Conference on Information and Knowledge Management, 2000, 86-93

40
Goldman S, Zhou Y. Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th International Conference on Machine Learning. 2000, 327-334

41
Zhou Z H, Li M. Semi-supervised regression with cotraining. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence. 2005, 908-913

42
Zhou Z H, Li M. Semi-supervised regression with cotraining style algorithms. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1479-1493

DOI

43
Mohamed T A, Gayar N El, Atiya A F. A cotraining approach for time series prediction with missing data. In: Proceedings of the 7th International Workshop on Multiple Classifier Systems. 2007, 93-102

DOI

44
Wang W, Zhou Z H. Analyzing co-training style algorithms. In: Proceedings of the 18th European Conference on Machine Learning, 2007, 454-465

45
Hwa R, Osborne M, Sarkar A, Steedman M. Corrected cotraining for statistical parsers. In: Working Notes of the ICML’03Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining. 2003

46
Pierce D, Cardie C. Limitations of co-training for natural language learning from large data sets. In: Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing. 2001, 1-9

47
Sarkar A. Applying co-training methods to statistical parsing. In: Proceedings of the 2nd Annual Meeting of the North American Chapter of the Association for Computational Linguistics. 2001, 95-102

48
Steedman M, Osborne M, Sarkar A, Clark S, Hwa R, Hockenmaier J, Ruhlen P, Baker S, Crim J. Bootstrapping statistical parsers from small data sets. In: Proceedings of the 11th Conference on the European Chapter of the Association for Computational Linguistics. 2003, 331-338

49
Li M, Li H, Zhou Z H. Semi-supervised document retrieval. Information Processing and Management, 2009, 45(3): 341-355

DOI

50
Mavroeidis D, Chaidos K, Pirillos S, Christopoulos D, Vazirgiannis M. Using tri-training and support vector machines for addressing the ECML-PKDD 2006 Discovery Challenge. In: Proceedings of ECMLPKDD 2006 Discovery Challenge Workshop. 2006, 39-47

51
Kockelkorn M, Lneburg A, Scheffer T. Using transduction and multi-view learning to answer emails. In: Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases. 2003, 266-277

52
Zhou Z H, Chen K J, Dai H B. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 2006, 24(2): 219-244

DOI

53
Zhou Z H, Chen K J, Jiang Y. Exploiting unlabeled data in content-based image retrieval. In: Proceedings of the 15th European Conference on Machine Learning. 2004, 525-536

54
Wang W, Zhou Z H. On multi-view active learning and the combination with semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning. 2008, 1152-1159

DOI

55
Muslea I, Minton S, Knoblock C A. Active learning with multiple views. Journal of Artificial Intelligence Research, 2006, 27(1): 203-233

56
Zhou Z H, Zhan D C, Yang Q. Semi-supervised learning with very few labeled training examples. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence. 2007, 675-680

57
Hotelling H. Relations between two sets of variates. Biometrika, 1936, 28(4): 321-377

58
Hardoon D R, Szedmak S, Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 2004, 16(12): 2639-2664

DOI

59
Zhang M L, Zhou Z H. Classifier ensemble with unlabeled data. CORR abs10909.3593, 2009

60
Zhang M L, Zhou Z H. Exploiting unlabeled data to enhance ensemble diversity. In: Proceedings of the 9th IEEE International Conference on Data Mining. 2010

DOI

61
Xu L, Amari S. Combining classifiers and learning mixtureof-experts. In: Dopico J R R, Dorado J, Pazos A, eds. Encyclopedia of Artificial Intelligence. 2009, 318-326

Outlines

/