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Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset

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Applications of Evolutionary Computation (EvoApplications 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6624))

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Abstract

In this paper a data mining approach for variable selection and knowledge extraction from datasets is presented. The approach is based on unguided symbolic regression (every variable present in the dataset is treated as the target variable in multiple regression runs) and a novel variable relevance metric for genetic programming. The relevance of each input variable is calculated and a model approximating the target variable is created. The genetic programming configurations with different target variables are executed multiple times to reduce stochastic effects and the aggregated results are displayed as a variable interaction network. This interaction network highlights important system components and implicit relations between the variables. The whole approach is tested on a blast furnace dataset, because of the complexity of the blast furnace and the many interrelations between the variables. Finally the achieved results are discussed with respect to existing knowledge about the blast furnace process.

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Author information

Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austria University of Applied Sciences, Campus Hagenberg Softwarepark 11, 4232, Hagenberg, Austria

    Michael Kommenda, Gabriel Kronberger & Michael Affenzeller

  2. voestalpine Stahl GmbH, voestalpine-Straße 3, 4020, Linz, Austria

    Christoph Feilmayr

Authors
  1. Michael Kommenda
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  2. Gabriel Kronberger
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  3. Christoph Feilmayr
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  4. Michael Affenzeller
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  3. Departamento de Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  4. Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. Knowledge Engineering Research Group, Aston University, Aston Triangle, B4 7ET, Birmingham, UK

    Anikó Ekárt

  6. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Edif. 8G Acceso B, 46022, Valencia, Spain

    Anna I. Esparcia-Alcázar

  7. Departamento de Electrónica y Tecnología de los Computadores, Universidad de Granada, 18071, Grenada, Spain

    Juan J. Merelo

  8. Department of Mathematical Information Technology, University of Jyväskylä, 4th Floor, P.O. Box 25, 40014, Agora, Finland

    Ferrante Neri

  9. Lehrstuhl für Algorithm Engineering, Technische Universität Dortmund, Otto-Hahn-Straße 14, 44227, Dortmund, Germany

    Mike Preuss

  10. Fakultät Elektrotechnik und Informationstechnik, HTWK Leipzig, Postfach 30 11 66, 04251, Leipzig, Germany

    Hendrik Richter

  11. Center for Computer Games Research, IT University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen S, Denmark

    Julian Togelius  & Georgios N. Yannakakis  & 

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© 2011 Springer-Verlag Berlin Heidelberg

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Kommenda, M., Kronberger, G., Feilmayr, C., Affenzeller, M. (2011). Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_28

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  • DOI: https://doi.org/10.1007/978-3-642-20525-5_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20524-8

  • Online ISBN: 978-3-642-20525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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