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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ince, K., & Klawonn, F. (2010). Attribute value selection considering the minimum description length approach and feature granularity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6178 LNAI, pp. 250–259). https://doi.org/10.1007/978-3-642-14049-5_26
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