Automatic construction of decision trees for classification

Automatic construction of decision trees for classification

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Article ID: iaor1995167
Country: Switzerland
Volume: 52
Issue: 1
Start Page Number: 231
End Page Number: 247
Publication Date: Sep 1994
Journal: Annals of Operations Research
Authors: ,
Keywords: decision theory
Abstract:

An algorithm for learning 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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