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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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
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