Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Improving the Combination Module with a Neural Network

  • Conference paper
Intelligent Computing (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

Included in the following conference series:

Abstract

In this paper we propose two versions of Stacked Generalization as the combination module of an ensemble of neural networks. The first version only uses the information provided by expert networks. The second one uses the information provided by experts and the input data of the pattern that is being classified. Finally, we have performed a comparison among 6 classical combination methods and the two versions of Stacked Generalization in order to get the best method. The results show that the methods based on Stacked Generalization are better than classical combination methods.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

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. Tumer, K., Ghosh, J.: Error Correlation and Error Reduction in Ensemble Classifiers. Connection Science 8(3-4), 385–403 (1996)

    Article  Google Scholar 

  2. Raviv, Y., Intratorr, N.: Bootstrapping with Noise: An Effective Regularization Technique. Connection Science, Special issue on Combining Estimators 8, 356–372 (1996)

    Google Scholar 

  3. Hernandez-Espinosa, C., Fernandez-Redondo, M., Torres-Sospedra, J.: Ensembles of Multilayer Feedforward for Classification Problems. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 744–749. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. Hernandez-Espinosa, C., Torres-Sospedra, J., Fernandez-Redondo, M.: New Experiments on Ensembles of Multilayer Feedforward for Classification Problems. In: Proceedings of International Conference on Neural Networks, IJCNN 2005, Montreal, Canada, pp. 1120–1124 (2005)

    Google Scholar 

  5. Torres-Sospedra, J., Fernandez-Redondo, M., Hernandez-Espinosa, C.: A Research on Combination Methods for Ensembles of Multilayer Feedforward. In: Proceedings of International Conference on Neural Networks, IJCNN 2005, Montreal, Canada, pp. 1125–1130 (2005)

    Google Scholar 

  6. Xu, L., Krzyzak, A., Suen, C.: Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Transactions on Systems, Man, and Cybernetics 22(3), 418–435 (1992)

    Article  Google Scholar 

  7. Verikas, A., Lipnickas, A., Malmqvist, K., Bacauskiene, M., Gelzinis, A.: Soft Combination of Neural Classifiers: A Comparative Study. Pattern Recognition Letters 20(4), 429–444 (1999)

    Article  Google Scholar 

  8. Jimenez, D., Walsh, N.: Dynamically Weighted Ensemble Neural Networks for Classification. IEEE World Congress on Computational Intelligence 1, 753–756 (1998)

    Google Scholar 

  9. Wolpert, D.H.: Stacked Generalization. Neural Networks 5(6), 1289–1301 (1994)

    MathSciNet  Google Scholar 

  10. Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hernández-Espinosa, C., Torres-Sospedra, J., Fernández-Redondo, M. (2006). Improving the Combination Module with a Neural Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_15

Download citation

  • DOI: https://doi.org/10.1007/11816157_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics