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Attribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity

  • Conference paper
Computational Intelligence for Knowledge-Based Systems Design (IPMU 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6178))

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Abstract

In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum description length principle (MDL) and aspects of granular computing. The approach is verified by giving an example using a data set which is extracted and preprocessed from an operational information system of the Components Toolshop of Volkswagen AG.

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References

  1. Wei-Yin, L.: Classification and Regression Tree Methods. In: Encyclopedia of Statistics in Quality and Reliability, pp. 315–323. Wiley-VCH, Chichester (2008)

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  5. Example of a Regression Tree, http://www.resample.com/xlminer/help/rtree/rtree_.htm

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Author information

Authors and Affiliations

  1. Volkswagen AG, Komponenten-Werkzeugbau, Gifhornerstr. 180, 38037, Braunschweig, Germany

    Kemal Ince

  2. Data Analysis and Pattern Recognition Lab, Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, 38302, Wolfenbüttel, Germany

    Frank Klawonn

Authors
  1. Kemal Ince
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  2. Frank Klawonn
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Editor information

Editors and Affiliations

  1. Fachbereich Mathematik und Informatik, Philipps-Universität Marburg, Hans-Meerwein-Str., 35032, Marburg, Germany

    Eyke Hüllermeier

  2. Fakultät Informatik, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Rudolf Kruse

  3. Fakultät für Elektrotechnik und Informationstechnik, Technische Universität Dortmund, Otto-Hahn-Str. 4, 44227, Dortmund, Germany

    Frank Hoffmann

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© 2010 Springer-Verlag Berlin Heidelberg

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Ince, K., Klawonn, F. (2010). Attribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_26

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  • DOI: https://doi.org/10.1007/978-3-642-14049-5_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14048-8

  • Online ISBN: 978-3-642-14049-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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