Skip to main content

Semi-supervised Multiple Classifier Systems: Background and Research Directions

  • Conference paper
Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

Included in the following conference series:

  • 2004 Accesses

Abstract

Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised classification has not received much attention. Few works on semi-supervised multiple classifiers appeared in the machine learning literature. This paper’s goal is to review the background results that can be exploited to promote research on semi-supervised multiple classifier systems, and to outline some future research directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Multiple Classifier Systems, LNCS, vols. 1857 (2000), 2096 (2001), 2364 (2002), 2709 (2003), 3077 (2004) Springer Verlag, Heidelberg

    Google Scholar 

  2. Fred, A.: Finding consistent clusters in data partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 309–318. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  3. Ghosh, J.: Multiclassifier systems: back to the future. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 1–15. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  4. Lang, K.: Newsweeder: learning to filter netnews. In: Machine Learning: Proceeding of the Twelfth International Conference (ICML 1995), pp. 331–339 (1995)

    Google Scholar 

  5. Jackson, Q., Landgrebe, D.: An adaptive classifier design for high-dimensional data analysis with a limited training data set. IEEE Trans. on Geoscience and Remote Sensing 39(12), 2664–2679 (2001)

    Article  Google Scholar 

  6. Cohen, I., Cozman, F.G., Sebe, N., Cirelo, M.C., Huang, T.: Semi-supervised learning of classifiers: theory, algorithms and their applications to human-computer interaction. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(12), 1553–1567 (2004)

    Article  Google Scholar 

  7. Martinez, C., Fuentes, O.: Face recognition using unlabeled data, Computacion y Sistems, Iberoamerican. Journal of Computer Science Research 7(2), 123–129 (2003)

    Google Scholar 

  8. Stolfo, S.J., Wenke, L., Chan, P.K., Fan, W., Eskin, E.: Data mining-based intrusion detectors: an overview of the Columbia IDS project. SIGMOD Record 30(4), 5–14 (2001)

    Article  Google Scholar 

  9. Seeger, M.: Learning with labeled and unlabeled data, Technical Report, University of Edinburgh, Institute for Adaptive and Neural Computation, pp. 1-62 (December 2002)

    Google Scholar 

  10. Miller, D.J., Uyar, H.S.: A mixture of experts classifier with learning based on both labeled and unlabeled data. In: Neural Information Processing Systems Foundation, NIPS 1997 (1997)

    Google Scholar 

  11. d’Alchè-Buc, F., Grandvalet, Y., Ambroise, C.: Semi-supervised marginboost, Neural Information Processing Systems Foundation. In: NIPS 2002 (2002)

    Google Scholar 

  12. Bennet, K., Demiriz, A., Maclin, R.: Exploiting unlabeled data in ensemble methods. In: Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 289–296 (2002)

    Google Scholar 

  13. Melville, P., Mooney, R.: Diverse ensembles for active learning. In: 21th Int. Conf. on Machine Learning, Canada, July 2004, Article no. 74 (2004)

    Google Scholar 

  14. Kemp, T., Waibel, A.: Unsupervised Training of a Speech Recognizer: Recent Experiments. In: Proc. Eurospeech, vol. 6, pp. 2725–2728 (1999)

    Google Scholar 

  15. Nagy, G., Shelton, G.L.: Self-corrective character recognition systems. IEEE Trans. on Information Theory, IT 12(2), 215–222 (1966)

    Article  Google Scholar 

  16. Nagy, G.: Classifiers that improve with use. In: Proc. Conference on Pattern Recognition and Multimedia, IEICE, Tokyo, February 2004, pp. 79–86 (2004)

    Google Scholar 

  17. Inoue, M., Ueda, N.: Exploitation of unlabeled sequences in hidden markov models. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(12), 1570–1581 (2003)

    Article  Google Scholar 

  18. Young, T.Y., Farjo, A.: On decision directed estimation and stochastic approximation. IEEE Trans. on Information Theory, 671–673 (September 1972)

    Google Scholar 

  19. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  21. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory, pp. 92–100 (1998)

    Google Scholar 

  22. Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proc. 5th Int. Conf. on Information and Knowledge Management, pp. 86–93 (2000)

    Google Scholar 

  23. Castelli, V., Cover, T.M.: On the exponential value of labeled samples. Pattern Recognition Letters 16, 105–111 (1995)

    Article  Google Scholar 

  24. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley&Sons Pub., West Sussex (2004)

    Book  MATH  Google Scholar 

  25. Raudys, S.: Experts’ boasting in trainable fusion rules. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(9), 1178–1182 (2003)

    Article  Google Scholar 

  26. Raudys, S., Roli, F.: The Behaviour Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 55–64. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  27. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)

    Article  Google Scholar 

  28. Goldman, S., Zhou, Y.: Enhancing supervised learning with unlabeled data. In: Proc. of the 7th Int. Con. on Machine Learning, ICML 2000, pp. 327–334 (2000)

    Google Scholar 

  29. Kuncheva, L.I.: Classifier ensembles for changing environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  30. Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.: Boosting, bagging, and consensus based classification of multisource remote sensing data. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 279–288. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  31. Jordan, M.I., Jacobs, R.A.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181–214 (1994)

    Article  Google Scholar 

  32. Agogino, A.K.: Design and control of large collections of learning agents, PhD thesis, The University of Texas at Austin (2003)

    Google Scholar 

  33. Wolpert, D.H., Tumer, K.: Optimal payoff functions for members of collectives. Advances in Complex Systems 4(2/3), 265–279 (2001)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Roli, F. (2005). Semi-supervised Multiple Classifier Systems: Background and Research Directions. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_1

Download citation

  • DOI: https://doi.org/10.1007/11494683_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics