Combining labelled and unlabelled data in the design of pattern classification systems

International Journal of Approximate Reasoning

Volumn 35, Issue 3, 2004, Pages 251-273

  Gabrys, Bogdan ^a   Petrakieva, Lina ^b  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

Combined learning methods; Pattern classification; Preliminary selection; Random selection; Semi supervised clustering; Supervised learning; Unsupervised learning

Indexed keywords

LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; RANDOM PROCESSES; SAMPLING;

COMBINED LEARNING METHODS; PRELIMINARY SELECTION; RANDOM SELECTION; SEMI-SUPERVISED CLUSTERING; SUPERVISED LEARNING; UNSUPERVISED LEARNING;

PATTERN RECOGNITION;

EID: 1142303894     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2003.08.005     Document Type: Conference Paper

References (23)

1. Bennett K.P., Demiriz A. Semi-supervised support vector machines. Proceedings of Neural Information and Processing System. 1998;368-374.
2. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, Available from 〈 www.ics.uci.edu/mlearn/MLRepository.html 〉 University of California, Department of Information and Computer Science, Irvine, CA, 1998.
3. A. Blum, T. Mitchell, Combining labelled and unlabelled data with co-training, in: Proceedings of COLT'1998, 1998, pp. 92-100.
4. R. Caruana, D. Cohn, A. McCallum, Semi-supervised clustering with user feedback, in: Machines that Learn, Snowbird, UT, USA, 2000.
5. R. Dara, S.C. Kremer, D.A. Stacey, Clustering unlabelled data with SOMs improves classification of labelled real-world data, in: Proceedings of the 2002 IEEE World Congress on Computational Intelligence, 2002, pp. 2237-2242.
6. R.P.W. Duin, Pattern recognition tools for Matlab, Available from 〈 ftp.ph.tn.tudelft.nl/pub/bob/prtools/ 〉, 2000.
7. Everitt B. Cluster Analysis. second ed. 1981;Halsted Press, New York.
8. Gabrys B. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. International Journal of Approximation and Reasoning. 30(3):2002;149-179.
9. Gabrys B., Bargiela A. General fuzzy min-max neural network for clustering and classification. IEEE Transactions on Neural Networks. 11(3):2000;769-783.
10. Ghahramani Z., Jordan M.I. Supervised learning from incomplete data via an EM approach. Advances in Neural Information Processing Systems. 1994;120-127.
11. S. Goldman, Y. Zhou, Enhancing supervised learning with unlabeled data, in: Proceedings of the Seventeenth ICML, 2000, pp. 327-334.
12. A.F. Gomez-Skarmeta, F. Jimenez, M. Valdes, J.A. Botia, A.M. Padilla, Towards a modelling framework for integrating hybrid techniques, in: Proceedings of the 2002 IEEE World Congress on Computational Intelligence, 2002, pp. 985-990.
13. Iyengar V.S., Apte C., Zhang T. Active learning using adaptive resampling. ACM SIGKDD. 2000.
14. R. Kothari, V. Jain, Learning from labeled and unlabeled data, in: IEEE World Congress on Computational Intelligence, IEEE International Joint Conference on Neural Networks, Honolulu, HI, USA, 2002, pp. 1468-1474.
15. T.M. Mitchell, The role of unlabeled data in supervised learning, in: Proceedings of the Sixth International Colloquium on Cognitive Science, Spain, 1999.
16. K. Nigam, R. Ghani, Analyzing the effectiveness and applicability of co-training, in: Ninth International CIKM, 2000, pp. 86-93.
17. K. Nigam, R. Ghani, Understanding the behaviour of co-training, in: Proceedings of the KDD-2000 Workshop on Text Mining, 2000.
18. Nigam K., McCallum A.K., Thrun S., Mitchell T. Text classification from labelled and unlabelled documents using EM. Machine Learning. 2000;103-134.
19. J. Park, H. Yae, Analysis of active feature selection in optic nerve data using labelled fuzzy C-means clustering, in: Proceedings of the 2002 IEEE World Congress on Computational Intelligence, 2002, pp. 1580-1585.
20. Pedrycz W., Waletzky J. Fuzzy clustering with partial supervision. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics. 27(5):1997;787-795.
21. D. Ruta, B. Gabrys, Analysis of the correlation between majority voting error and the diversity measures in multiple classifier systems, in: Proc. of the SOCO/ISFI 2001 Conference, Paper No.1824-025, 2001.
22. M. Seeger, Learning with labelled and unlabelled data, Technical Report, Edinburgh University, 2001.
23. Theodoridis S., Koutroumbas K. Pattern Recognition. 1999;Academic Press, New York.