Abstract
This paper presents a neural network tree regression system with dynamic optimization of input variable transformations and post-training optimization. The decision tree consists of MLP neural networks, which optimize the split points and at the leaf level predict final outputs. The system is designed for regression problems of big and complex datasets. It was applied to the problem of steel temperature prediction in the electric arc furnace in order to decrease the process duration at one of the steelworks.
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References
Yan-Qing, Z., Fu-lai, C.: A Fuzzy Neural Network Tree with Heuristic Backpropagation Learning. Neural Networks (2002)
Schetinin, V.: A Neural Network Decision Tree for Learning Concepts from EEG Data. In: NIMIA-SC (2001)
Golea, M., Marchand, M.: A Growth Algorithm for Neural Network Decision Trees. Europhysics Lett. (1990)
Foresti, G.L., Micheloni, C.: Generalized Neural Trees for Pattern Classification. IEEE Transactions on Neural Networks 13(6) (2002)
Fong, S., et al.: Applying a Hybrid Model of Neural Network and Decision Tree Classifier for Predicting University Admission. In: ICICS (2009)
Chen, Y., et al.: Time-series forecasting using flexible neural tree model. Information Sciences (2004)
Kordos, M., Blachnik, M., et al.: A Hybrid System with Regression Trees in Steelmaking Process. In: HAIS (2011)
Wieczorek, T., Kordos, M.: Neural Network-based Prediction of Additives in the Steel Refinement Process. Computer Methods in Materials Science 10(1) (2010)
Wieczorek, T., Blachnik, M., Maczka, K.: Building a model for time reduction of steel scrap meltdown in the electric arc furnace (EAF): General strategy with a comparison of feature selection methods. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 1149–1159. Springer, Heidelberg (2008)
Kendall, M., et al.: A window into the electric arc furnace, a continuous temperature sensor measuring the complete furnace cycle. Archives of Metallurgy and Materials 53(2), 451–454 (2008)
Millman, M.S., et al.: Direct observation of the melting process in an EAF with a closed slag door. Archives of Metallurgy and Materials 53(2), 463–468 (2008)
Wendelstorf, J.: Analysis of the EAF operation by process modeling. Archives of Metallurgy and Materials 53(2), 385–390 (2008)
Fernandez, J., et al.: Online estimation of electric arc furnace tap temperature by using fuzzy neural networks. Engineering Applications of Artificial Intelligence 21 (2008)
Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 15–27. Springer, Heidelberg (2002)
Kordos, M., Duch, W.: Variable step search algorithm for feedforward networks. Neurocomputing 71(13-15), 2470–2480 (2008)
Setiono, R., Thong, J.: An approach to generate rules from neural networks for regression problems. European Journal of Operational Research 155(1) (2004)
Czajkowski, M., Krętowski, M.: Globally induced model trees: An evolutionary approach. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 324–333. Springer, Heidelberg (2010)
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Kordos, M., Blachnik, M., Wieczorek, T. (2011). Temperature Prediction in Electric Arc Furnace with Neural Network Tree. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_10
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DOI: https://doi.org/10.1007/978-3-642-21738-8_10
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