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Automatic construction of decision trees for classification

  • Algorithms For Decision Problems
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Abstract

An algorithm for learning decision trees for classification and prediction is described which converts real-valued attributes into intervals using statistical considerations. The trees are automatically pruned with the help of a threshold for the estimated class probabilities in an interval. By means of this threshold the user can control the complexity of the tree, i.e. the degree of approximation of class regions in feature space. Costs can be included in the learning phase if a cost matrix is given. In this case class dependent thresholds are used.

Some applications are described, especially the task of predicting the high water level in a mountain river.

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Authors and Affiliations

  1. Fraunhofer Institut IITB, Einrichtung für Prozeßoptimierung, Berlin, Germany

    Wolfgang Müller & Fritz Wysotzki

Authors
  1. Wolfgang Müller
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  2. Fritz Wysotzki
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Müller, W., Wysotzki, F. Automatic construction of decision trees for classification. Ann Oper Res 52, 231–247 (1994). https://doi.org/10.1007/BF02032305

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  • DOI: https://doi.org/10.1007/BF02032305

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