DCPE co-training for classification

Neurocomputing

Volumn 86, Issue , 2012, Pages 75-85

  Xu, Jin ^a   He, Haibo ^b   Man, Hong ^a  

^a Stevens Institute of Technology   (United States)

^b University of Rhode Island   (United States)

Author keywords

Class probability estimation; Classification; Co training; Diversity; Machine learning; Semi supervised learning

Indexed keywords

CLASS PROBABILITIES; CO-TRAINING; DIVERSITY; MACHINE-LEARNING; SEMI-SUPERVISED LEARNING;

SUPERVISED LEARNING;

CLASSIFICATION (OF INFORMATION);

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; CLASSIFICATION; CLASSIFIER; DECISION TREE; DIVERSITY OF CLASS PROBABILITY ESTIMATION COTRAINING; INTERMETHOD COMPARISON; K NEAREST NEIGHBOR; LEARNING ALGORITHM; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

EID: 84862789001     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.01.006     Document Type: Article

References (47)

1. Cohen I., Cozman F.G., Sebe N., Cirelo M.C., Huang T.S. Semisupervised learning of classifiers: theory, algorithm, and their application to human-computer interaction. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(12):1553-1567.
2. X. Zhu, Semi-Supervised Learning Literature Survey, Computer Sciences Technical Report, University of Wisconsin, Madison, 2006.
3. A. Dong, B. Bhanu, A new semi-supervised EM algorithm for image retrieval, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Madison, WI, 2003, pp. 662-667.
4. Wang C., Zhao J., He X.F., Chen C., Bu J.J. Image retrieval using nonlinear manifold embedding. Neurocomputing 2009, 72:3922-3929.
5. F. Tang, S. Brennan, Q. Zhao, H. Tao, Co-tracking using semi-supervised support vector machines, in: Proceedings of the IEEE International Conference on Computer Vision, 2007, pp. 1-8.
6. Scheffer T. Email answering assistance by semi-supervised text classification. Intell. Data Anal. 2004, 8(5):481-493.
7. A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the 11th Annual Conference on Computational Learning Theory, 1998, pp. 92-100.
8. Teller A., Veloso M. Program evolution for data mining. Int. J. Expert Syst. 1995, 8:216-236.
9. Zhou Z.H., Li M. Semi-supervised learning by disagreement. Knowl. Inf. Syst. 2010, 24(3):415-439.
10. S. Goldman, Y. Zhou, Enhancing supervised learning with unlabeled data, in: Proceedings of the 17th International Conference on Machine Learning, 2000, pp. 327-334.
11. Y. Zhou, S. Goldman, Democratic co-learning, in: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04), 2004, pp. 594-202.
12. Brown G., Wyatt J., Harris R., Yao X. Diversity creation methods: a survey and categorization. Inf. Fusion 2005, 6(1):5-20.
13. Li T., Ogihara M. Semisupervised learning from different information sources. Knowl. Inf. Syst. 2005, 7(3):289-309.
14. Dietterich T.G. Ensemble methods in machine learning. First International Workshop on Multiple Classifier Systems, Cagliari, Italy, Lecture Notes in Computer Science 2000, vol. 1857:1-15. Springer, Berlin.
15. Wu X.D., Kumar V., et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2007, 14(1):1-37.
16. Dimitrakakis C., Bengio S. Online adaptive policies for ensemble classifiers. Neurocomputing 2005, 64:211-221.
17. M.F.A. Hady, F. Schwenker, Co-training by committee: a new semi-supervised learning framework, in: Proceedings of the IEEE International Conference on Data Mining Workshops, 2008.
18. A. Frank, A. Asuncion, UCI machine learning repository, URL, 2010. http://archive.ics.uci.edu/ml.
19. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 1977, 39(1):1-38.
20. T. De Bie, N. Cristianini, Convex method for transduction, in: NIPS 16, 2004.
21. A. Blum, S. Chawla, Learning from labeled and unlabeled data using graph mincuts, in: Proceedings of the 18th International Conference on Machine Learning, 2001, pp. 19-26.
22. D. Yarowsky, Unsupervised word sense disambiguation rivaling supervised methods, in: Proceedings of the 33rd Annual Meeting of the Association on Computational Linguistics, 1995, pp. 189-196.
23. J. Chan, I. Koprinska, J. Poon, Co-training with a single natural feature set applied to email classification, in: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society Washington, DC, 2004, pp. 586-589.
24. S. Dasgupta, M. Littman, D. McAllester, PAC generalization bounds for co-training, in: Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge, MA, 2002, pp. 375-382.
25. Balcan M.F., Blum A., Yang K. Co-training and expansion: towards bridging theory and practice. Advances in Neural Information Processing Systems 2005, vol. 17:89-96. MIT Press, Cambridge.
26. Balcan M.F., Blum A. An Augmented PAC Model for Semi-Supervised Learning, Semi-Supervised Learning 2005, MIT Press.
27. Zhou Z.H., Li M. Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng. 2005, 17(11):1529-1541.
28. M. Collins, Y. Singer, Unsupervised models for named entity classification, in: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, College Park, MD, 1999, pp. 100-110.
29. R. Ghani, Combining labeled and unlabeled data for text classification with a large number of categories, in: Proceedings of the IEEE International Conference on Data Mining, 2001.
30. A. Levin, P. Viola, Y. Freund, Unsupervised improvement of visual detectors using co-training, in: Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003, pp. 626-633.
31. A. Sarkar, 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, Pittsburgh, PA, 2001, pp. 95-102.
32. Li M., Zhou Z.H. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. Man Cybern. A 2007, 37(6):1088-1098.
33. Saar-Tsechansky M., Provost F. Active sampling for class probability estimation and ranking. Mach. Learn. 2004, 54:153-178.
34. B. Zadrozny, C. Elkan, Learning and making decisions when costs and probabilities are both unknown, in: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 204-212.
35. D. Margineantu, Class probability estimation and cost-sensitive classification decisions, in: Proceedings of the 13th European Conference on Machine Learning, 2002, pp. 270-281.
36. Guerrero-Curieses A., Cid-Sueiro J., Alaiz-Rodriguez R., Figueiras-Vidal A. Local estimation of posterior class probabilities to minimize classification errors. IEEE Trans. Neural Networks 2004, 15(2):309-317.
37. Atiya A. Estimating the posterior probabilities using the K-nearest neighbor rule. Neural Comput. 2005, 17:731-740.
38. J. Xu, H. He, H. Man, DCPE Co-training: co-training based on diversity of class probability estimation, in: Proceedings of the 2010 International Joint Conference on Neural Networks.
39. Z. Qin, Naive Bayes classification given probability estimation trees, in: Proceedings of the 5th International Conference on Machine Learning and Applications, 2006.
40. W. Wei, E. Barnard, M. Fanty, Improved probability estimation with neural network models, in: Proceedings of the International Conference on Spoken Language Systems, 1996, pp. 498-501.
41. He H., Garcia E.A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 2009, 21(9):1263-1284.
42. Fukunaga K., Hostetler L. k-Nearest-neighbor Bayes-risk estimation. IEEE Trans. Inf. Theory 1975, 21(3):285-293.
43. Angluin D., Laird P. Learning from noisy examples. Mach. Learn. 1988, 2:343-370.
44. W. Wang, Z.H. Zhou, Analyzing co-training style algorithms, in: Proceedings of the 18th European Conference on Machine Learning, Warsaw, Poland, 2007, pp. 454-465.
45. Schuurmans D., Southey F. Metric-based methods for adaptive model selection and regularization. Mach. Learn. 2001, 48:51-84. (Special Issue on New Methods for Model Selection and Model Combination).
46. Demsar J. Statistical comparisons of classifiers over multiple datasets. J. Mach. Learn. Res. 2006, 7:1-30.
47. S. Gupta, J. Kim, K. Grauman, R. Mooney, Watch, listen and learn: co-training on captioned images and videos, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Antwerp, Belgium, 2008, pp. 457-472.