A hybrid system with regression trees in steel-making process

Authors:
Mirosław Kordos;Marcin Blachnik;Marcin Perzyk;Jacek Kozłowski;Orestes Bystrzycki;Mateusz Gródek;Adrian Byrdziak;Zenon Motyka
Affiliations:
University of Bielsko-Biala, Department of Mathematics and Informatics, Bielsko-Biała, Willowa, Poland;Silesian University of Technology, Department of Management and Informatics, Katowice, Krasinskiego, Poland;Warsaw University of Technology, Faculty of Production Engineering, Warsaw, Poland;Warsaw University of Technology, Faculty of Production Engineering, Warsaw, Poland;University of Bielsko-Biala, Department of Mathematics and Informatics, Bielsko-Biała, Willowa, Poland;University of Bielsko-Biala, Department of Mathematics and Informatics, Bielsko-Biała, Willowa, Poland;University of Bielsko-Biala, Department of Mathematics and Informatics, Bielsko-Biała, Willowa, Poland;University of Bielsko-Biala, Department of Mathematics and Informatics, Bielsko-Biała, Willowa, Poland
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

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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.