Advertisement

Combining the Advantages of Neural Networks and Decision Trees for Regression Problems in a Steel Temperature Prediction System

  • Miroslaw Kordos
  • Piotr Kania
  • Pawel Budzyna
  • Marcin Blachnik
  • Tadeusz Wieczorek
  • Slawomir Golak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7209)

Abstract

Simple decision trees enable obtaining simple logical rules with a limited accuracy in regression tasks. Neural networks as highly non-linear systems can map much more complex shapes and thus can obtain higher prediction accuracy in regression problems, that is however at the cost of the poor comprehensibility of the decision process. We present a hybrid system which incorporates the features of both a regression tree and a neural network. This system allowed for achieving high prediction accuracy supported by comprehensive logical rules for the practical problem of temperature prediction in electric arc furnace at one of the steelwors.

Keywords

neural networks decision tree regression logical rules 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Corchado, E., et al.: Hybrid intelligent algorithms and applications. Information Science 180(14), 2633–2634 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Blake, C., Keogh, E., Merz, C.: UCI Repository of Machine Learning Databases (1998-2011), http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength
  5. 5.
    Blake, C., Keogh, E., Merz, C.: UCI Repository of Machine Learning Databases (1998-2011), http://archive.ics.uci.edu/ml/datasets/Communities+and+Crime
  6. 6.
    Kordos, M., Blachnik, M., Perzyk, M., Kozłowski, J., Bystrzycki, O., Gródek, M., Byrdziak, A., Motyka, Z.: A Hybrid System with Regression Trees in Steel-Making Process. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part I. LNCS (LNAI), vol. 6678, pp. 222–230. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Kordos, M., Blachnik, M., Wieczorek, T., Golak, S.: Neural Network Committees Optimized with Evolutionary Methods for Steel Temperature Control. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS (LNAI), vol. 6922, pp. 42–51. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Kordos, M., Blachnik, M., Wieczorek, T.: Evolutionary Optimization of Regression Model Ensembles in Steel-Making Process. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 369–376. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Setiono, R., Thong, J.: An approach to generate rules from neural networks for regression problems. European Journal of Operational Research 155(1) (2004)Google Scholar
  10. 10.
    Wang, J., et al.: Regression rules extraction from artificial neural network based on least squares. In: 7th Int. Conference on Natural Computation (ICNC), Shanghai (2011)Google Scholar
  11. 11.
    Saitoa, K., Nakano, R.: Extracting regression rules from neural networks. Neural Networks 15, 1279–1288 (2002)CrossRefGoogle Scholar
  12. 12.
    Markowska-Kaczmar, U., Mularczyk, K.: GA-Based Rule Extraction from Neural Networks for Approximation. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 141–148 (2006)Google Scholar
  13. 13.
    Kordos, M.: Neural Network Regression for LHF Process Optimization. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5506, pp. 453–460. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Blachnik, M., Mączka, K., Wieczorek, T.: A Model for Temperature Prediction of Melted Steel in the Electric Arc Furnace(EAF). In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 371–378. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Duch, W., Setiono, R., Zurada, J.: Computational intelligence methods for understanding of data. Proceedings of the IEEE 92(5), 771–805 (2008)CrossRefGoogle Scholar
  16. 16.
    Kordos, M., Duch, W.: Variable Step Search Algorithm for Feedforward Networks. Neurocomputing 71(13-15), 2470–2480 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miroslaw Kordos
    • 1
  • Piotr Kania
    • 1
  • Pawel Budzyna
    • 1
  • Marcin Blachnik
    • 2
  • Tadeusz Wieczorek
    • 2
  • Slawomir Golak
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of Bielsko-BialaBielsko-BialaPoland
  2. 2.Department of Management and InformaticsSilesian University of TechnologyKatowicePoland

Personalised recommendations