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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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
Online ISBN: 978-3-642-21219-2
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