Skip to main content
SpringerLink
Log in
Book cover

International Conference on Hybrid Artificial Intelligence Systems

HAIS 2012: Hybrid Artificial Intelligent Systems pp 409–420Cite as

Evolutionary Optimized Forest of Regression Trees: Application in Metallurgy

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7208))

Abstract

A forest of regression trees is generated, with each tree using a different randomly chosen subset of data. Then the forest is optimized in two ways. First each tree independently by shifting the split points to the left or to the right to compensate for the fact, that the original split points were set up as being optimal only for the given node and not for the whole tree. Then evolutionary algorithms are used to exchange particular tree subnodes between different trees in the forest. This leads to the best single tree, which although may produce not better results than the forest, but can generate comprehensive logical rules that are very important in some practical applications. The system is currently being applied in the optimization of metallurgical processes.

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. 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, vol. 6678, pp. 222–230. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  2. Duch, W., Setiono, R., Zurada, J.: Computational intelligence methods for understanding of data. Proceedings of the IEEE 92(5), 771–805 (2008)

    Article  Google Scholar 

  3. Jankowski, N., Grabczewski, K.: Heterogenous Committees with Competence Analysis. In: Fifth International Conference on Hybrid Intelligent Systems, Rio de Janeiro, Brasil, pp. 417–422 (2005)

    Google Scholar 

  4. Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)

    Article  Google Scholar 

  5. 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, vol. 6922, pp. 42–51. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Grąbczewski, K., Duch, W.: Heterogeneous Forests of Decision Trees. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 504–509. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. 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)

    Chapter  Google Scholar 

  8. 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 

  9. Tresp, V.: Committee Machines. Handbook for Neural Network Signal Processing. CRC Press (2001)

    Google Scholar 

  10. Breiman, L.: Combining predictors. In: Sharkey, A.J.C. (ed.) Combining Artificial Neural Nets. Springer, Heidelberg (1999)

    Google Scholar 

  11. Barbosa, B.H.G., Bui, L.T., Abbass, H.A., Aguirre, L.A., Braga, A.P.: Evolving an Ensemble of Neural Networks Using Artificial Immune Systems. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H.A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 121–130. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Chen, H., Yao, X.: Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning. IEEE Trans. on Knowledge and Data Engineering 22, 1738–1751 (2010)

    Article  Google Scholar 

  13. Corchado, E., et al.: Hybrid intelligent algorithms and applications. Information Science 180(14), 2633–2634 (2010)

    Article  MathSciNet  Google Scholar 

  14. Quinlan, J.: Learning with Continuous Classes. In: Proc. of AI 1992, pp. 343–348. World Scientific, Singapore (1992)

    Google Scholar 

  15. Malerba, D., Esposito, F., Ceci, M., Appice, A.: Top-down Induction of Model Trees with Regression and Splitting Nodes. IEEE Transactions on PAMI 26(5), 612–625 (2004)

    Article  Google Scholar 

  16. Czajkowski, M., Kretowski, M.: An Evolutionary Algorithm for Global Induction of Regression Trees with Multivariate Linear Models. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS(LNAI), vol. 6804, pp. 230–239. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Potts, D., Sammut, C.: Incremental Learning of Linear Model Trees. Machine Learning 62, 5–48 (2005)

    Article  Google Scholar 

  18. 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)

    Chapter  Google Scholar 

  19. http://www.kordos.com/his.html

  20. Landwehr, N., Hall, M., Frank, E.: Logistic Model Trees. Machine Learning 95 (2005)

    Google Scholar 

  21. Zelinka, I., et al.: Evolutionary Algorithms and Chaotic systems. Springer, Heidelberg (2010)

    Book  MATH  Google Scholar 

  22. Zelinka, I., Senkerik, R., Oplatkova, Z.: Evolutionary Scanning and Neural Network Optimization. In: 19th International Conference on Database and Expert Systems Application, DEXA, pp. 576–582 (2008)

    Google Scholar 

  23. Tsutsui, S., et al.: Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms. In: The 1999 Genetic and Evolutionary Computation Conference, pp. 657–664 (1999)

    Google Scholar 

  24. Semya, E., et al.: Multiple crossover genetic algorithm for the multiobjective traveling salesman problem. Electronic Notes in Discrete Mathematics 36, 939–946 (2010)

    Article  Google Scholar 

  25. Kordos, M., Duch, W.: Variable Step Search Algorithm for Feedforward Networks. Neurocomputing 71(13-15), 2470–2480 (2008)

    Article  Google Scholar 

  26. Blake, C., Keogh, E., Merz, C.: UCI Repository of Machine Learning Databases (1998-2011), http://archive.ics.uci.edu/ml/datasets/

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Bielsko-Biala, Bielsko-Biala, Willowa 2, Poland

    Mirosław Kordos, Jerzy Piotrowski & Szymon Bialka

  2. Department of Management and Informatics, Silesian University of Technology, Katowice, Krasinskiego 8, Poland

    Marcin Blachnik, Slawomir Golak & Tadeusz Wieczorek

Authors
  1. Mirosław Kordos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerzy Piotrowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Szymon Bialka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcin Blachnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Slawomir Golak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tadeusz Wieczorek
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VŠB-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs(MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, 120-749, Seoul, Korea

    Sung-Bae Cho

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kordos, M., Piotrowski, J., Bialka, S., Blachnik, M., Golak, S., Wieczorek, T. (2012). Evolutionary Optimized Forest of Regression Trees: Application in Metallurgy. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28942-2_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28941-5

  • Online ISBN: 978-3-642-28942-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation