Abstract
The paper presents a hybrid regresseion model with the main emphasis put on the regression tree unit. It discusses input and output variable transformation, determining the final decision of hybrid models and node split optimization of regression trees. Because of the ability to generate logical rules, a regression tree maybe the preferred module if it produces comparable results to other modules, therefore the optimization of node split in regression trees is discussed in more detail. A set of split criteria based on different forms of variance reduction is analyzed and guidelines for the choice of the criterion are discussed, including the trade-off between the accuracy of the tree, its size and balance between minimizing the node variance and keeping a symmetric structure of the tree. The presented approach found practical applications in the metallurgical industry.
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References
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)
Blachnik, M., Mączka, K., Wieczorek, T.: A model for temperature prediction of melted steel in the electric arc furnace(EAF). LNCS, vol. 6614 (2010)
Corchado, E., et al.: Hybrid intelligent algorithms and applications. Information Science 180(14), 2633–2634 (2010)
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Wozniak, M., Zmyslony, M.: Designing fusers on the basis of discriminants – evolutionary and neural methods of training. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 590–597. Springer, Heidelberg (2010)
Duch, W., Setiono, R., Zurada, J.: Computational intelligence methods for understanding of data. Proceedings of the IEEE 92(5), 771–805 (2008)
Landwehr, N., Hall, M., Frank, E.: Logistic Model Trees. Machine Learning 95 (2005)
Kohavi, R.: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
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)
Quinlan, J.R.: Simplifying decision trees. Int. Journal of Man-Machine Studies 27(3) (1987)
Maszczyk, T., Duch, W.: Comparison of Shannon, Renyi and Tsallis Entropy Used in Decision Trees. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 643–651. Springer, Heidelberg (2008)
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Kordos, M. et al. (2011). A Hybrid System with Regression Trees in Steel-Making Process. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_29
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DOI: https://doi.org/10.1007/978-3-642-21219-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21218-5
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