Abstract
Decision trees are helpful decision support tools, due to their graphical nature and the easiness to obtain them from data. Unfortunately, decision tree size tends to grow according to the complexity of the learning data, which may be problematic in real world settings. This paper proposes an original solution to reduce the size of decision trees by taking user preferences into account. More specifically, we present a user-driven algorithm that automatically transforms data in order to construct simpler decision tree. A prototype has been implemented, and the benefits are shown on several UCI datasets.
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© 2013 Springer-Verlag Berlin Heidelberg
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Parisot, O., Bruneau, P., Didry, Y., Tamisier, T. (2013). User-Driven Data Preprocessing for Decision Support. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2013. Lecture Notes in Computer Science, vol 8091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40840-3_13
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DOI: https://doi.org/10.1007/978-3-642-40840-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40839-7
Online ISBN: 978-3-642-40840-3
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