Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Multiple Classifier Systems

MCS 2002: Multiple Classifier Systems pp 1–15Cite as

Multiclassifier Systems: Back to the Future

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2364))

Abstract

While a variety of multiple classifier systems have been studied since at least the late 1950’s, this area came alive in the 90’s with significant theoretical advances as well as numerous successful practical applications. This article argues that our current understanding of ensemble-type multiclassifier systems is now quite mature and exhorts the reader to consider a broader set of models and situations for further progress. Some of these scenarios have already been considered in classical pattern recognition literature, but revisiting them often leads to new insights and progress. As an example, we consider how to integrate multiple clusterings, a problem central to several emerging distributed data mining applications. We also revisit output space decomposition to show how this can lead to extraction of valuable domain knowledge in addition to improved classification accuracy.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Selfridge, O.G.: Pandemonium: a paradigm for learning. Proc. of Symp. held at the National Physical Lab. (1958) 513–526

    Google Scholar 

  2. Nilsson, N.J.: Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw Hill, NY (1965)

    MATH  Google Scholar 

  3. Kanal, L.: Patterns in pattern recognition. IEEE Trans. Information Theory IT-20 (1974) 697–722

    Article  MathSciNet  Google Scholar 

  4. Minsky, M.: Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Magazine 12 (1991) 34–51

    Google Scholar 

  5. Granger, C.W.J.: Combining forecasts-twenty years later. Journal of Forecasting 8 (1989) 167–173

    Article  Google Scholar 

  6. French, S.: Group consensus probability distributions: A critical survey. In Bernardo et al., J.M., ed.: Bayesian Statistics 2. North Holland, New York (1985)

    Google Scholar 

  7. Benediktsson, J.A., Swain, P.H.: Consensus theoretic classification methods. IEEE Transactions on Systems, Man, and Cybernetics 22 (1992) 688–704

    Article  MATH  Google Scholar 

  8. Luo, R.C., Kay, M.G.: Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics 19 (1989) 901–931

    Article  Google Scholar 

  9. Narendra, K., Balakrishnan, J., Ciliz, K.: Adaptation and learning using multiple models, switching and tuning. IEEE Control Systems Magazine (1995) 37–51

    Google Scholar 

  10. Murray-Smith, R., Johansen, T.A.: Multiple Model Approaches to Modelling and Control. Taylor and Francis, UK (1997)

    Google Scholar 

  11. Ghosh, J., Deuser, L., Beck, S.: A neural network based hybrid system for detection, characterization and classification of short-duration oceanic signals. IEEE Jl. of Ocean Engineering 17 (1992) 351–363

    Article  Google Scholar 

  12. Sharkey, A.: On combining artificial neural networks. Connection Science 8 (1996) 299–314

    Article  Google Scholar 

  13. Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20 (1998) 226–239

    Article  Google Scholar 

  14. Tumer, K.: Linear and order statistics combiners for reliable pattern classification. PhD thesis, Dept. of ECE, Univ. of Texas at Austin, Dec. (1996)

    Google Scholar 

  15. Perrone, M.P.: Improving Regression Estimation: Averaging Methods for Variance Reduction with Extensions to General Convex Measure Optimization. PhD thesis, Brown University (1993)

    Google Scholar 

  16. Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation and active learning. In G. Tesauro, D.T., Leen, T., eds.: Advances in Neural Information Processing Systems-7. (1995) 231–238

    Google Scholar 

  17. Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 993–1000

    Article  Google Scholar 

  18. Tumer, K., Ghosh, J.: Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition 29 (1996) 341–348

    Article  Google Scholar 

  19. Tumer, K., Ghosh, J.: Ensemble correlation and error reduction in ensemble classifiers. Connection Science 8 (1996) 385–404

    Article  Google Scholar 

  20. Dietterich, T.G.: Ensemble methods in machine learning. In Kittler, J., Roli, F. eds.: Multiple Classifier Systems. LNCS Vol. 1857, Springer (2001) 1–15

    Chapter  Google Scholar 

  21. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical Report Dept. of Statistics, Stanford University. (1998)

    Google Scholar 

  22. Ghosh, J., Beck, S., Chu, C.: Evidence combination techniques for robust classification of short-duration oceanic signals. In: SPIE Conf. on Adaptive and Learning Systems, SPIE Proc. Vol. 1706. (1992) 266–276

    Google Scholar 

  23. Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics 22 (1992) 418–435

    Article  Google Scholar 

  24. Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (1994) 66–76

    Article  Google Scholar 

  25. Kittler, J., Roli (Eds.), F.: Multiple Classifier Systems. LNCS Vol. 1857, Springer (2001)

    MATH  Google Scholar 

  26. Jordan, M., Jacobs, R.: Hierarchical mixture of experts and the EM algorithm. Neural Computation 6 (1994) 181–214

    Article  Google Scholar 

  27. Holmstrom, L., Koistinen, P., Laaksonen, J., Oja, E.: Neural and statistical classifiers-taxonomy and two case studies. IEEE Transactions on Neural Networks 8 (1997) 5–17

    Article  Google Scholar 

  28. Ramamurti, V., Ghosh, J.: Structurally adaptive modular networks for non-stationary environments. IEEE Transactions on Neural Networks 10 (1999) 152–60

    Article  Google Scholar 

  29. Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 405–410

    Article  Google Scholar 

  30. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, New Jersey (1988)

    MATH  Google Scholar 

  31. Barthelemy, J.P., Laclerc, B., Monjardet, B.: On the use of ordered sets in problems of comparison and consensus of classifications. Jl. of Classification 3 (1986) 225–256

    Article  Google Scholar 

  32. Neumann, D.A., Norton, V.T.: Clustering and isolation in the consensus problem for partitions. Journal of Classification 3 (1986) 281–298

    Article  MATH  MathSciNet  Google Scholar 

  33. Kim, J., Warnow, T.: Tutorial on phylogenetic tree estimation. In: Intelligent Systems for Molecular Biology, Heidelberg. (1999)

    Google Scholar 

  34. Johnson, E., Kargupta, H.: Collective, hierarchical clustering from distributed, heterogeneous data. In Zaki, M., Ho, C., eds.: Large-Scale Parallel KDD Systems. Volume 1759 of LNCS., Springer-Verlag (1999) 221–244

    Chapter  Google Scholar 

  35. Fred, A.L.N., Jain, A.K.: Data clustering using evidence accumulation. In: Proc. ICPR. (2002) to appear

    Google Scholar 

  36. Bollacker, K.D., Ghosh, J.: Effective supra-classifiers for knowledge base construction. Pattern Recognition Letters 20(11–13) (1999) 1347–52

    Article  Google Scholar 

  37. Kargupta, H., Chan, P., eds.: Advances in Distributed and Parallel Knowledge Discovery. AAAI/MIT Press, Cambridge, MA (2000)

    Google Scholar 

  38. Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining partitionings. In: Proceedings of AAAI 2002, Edmonton, Canada, AAAI (2002) in press.

    Google Scholar 

  39. Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: Proc. AAAI Workshop on AI for Web Search (AAAI 2000), Austin, AAAI (2000) 58–64

    Google Scholar 

  40. Kumar, S.: Modular learning through output space decomposition. PhD thesis, Dept. of ECE, Univ. of Texas at Austin, Dec. (2000)

    Google Scholar 

  41. Anand, R., Methrotra, K., Mohan, C.K., Ranka, S.: Efficient classification for multiclass problems using modular neural networks. IEEE Transactions on Neural Networks 6 (1995) 117–125

    Article  Google Scholar 

  42. Berenstein, C., Kanal, L.N., Lavine, D.: Consensus rules. In Kanal, L.N., Lemmer, J.F., eds.: Uncertainty in Artificial Intelligence. North Holland, New York (1986)

    Google Scholar 

  43. Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In Jordan, M.I., Kearns, M.J., Solla, S.A., eds.: Advances in Neural Information Processing Systems. Volume 10., The MIT Press (1998)

    Google Scholar 

  44. Kumar, S., Crawford, M.M., Ghosh, J.: A versatile framework for labelling imagery with a large number of classes. In: Proc. IJCNN. (1999)

    Google Scholar 

  45. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via errorcorrecting output codes. Jl. of Artificial Intelligence Research 2 (1995) 263–286

    MATH  Google Scholar 

  46. Ricci, F., Aha, D.: Extending local learners with errorcorrecting output codes. Technical report, Technical Report 9701-08, IRST (1997)

    Google Scholar 

  47. Chakrabarti, S., Dom, B., Agrawal, R., Raghavan, P.: Scalable feature selection, classication and signature generation for organizing large text databases into hierarchical topic taxonomies. VLDB Journal 7 (1998) 163–178

    Article  Google Scholar 

  48. Kumar, S., Ghosh, J., Crawford, M.M.: Hierarchical fusion of multiple classifiers for hyperspectral data analysis”, Pattern Analysis and Applications, spl. Issue on Fusion of Multiple Classifiers 5 (2002) In Press

    Google Scholar 

  49. Thrun, S., Pratt, L.: Learning To Learn. Kluwer Academic, Norwell, MA (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Texas, Austin, TX, 78712-1084

    Joydeep Ghosh

Authors
  1. Joydeep Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Electrical and Electronical Engineering, University of Cagliari, Piazza D’Armi, 09123, Cagliari, Italy

    Fabio Roli

  2. Centre for Vision, Speech and Signal Processing, University of Surrey, Guilford, Surrey, GUZ 7XH, UK

    Josef Kittler

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ghosh, J. (2002). Multiclassifier Systems: Back to the Future. In: Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2002. Lecture Notes in Computer Science, vol 2364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45428-4_1

Download citation

  • DOI: https://doi.org/10.1007/3-540-45428-4_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43818-2

  • Online ISBN: 978-3-540-45428-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation