Automatic construction of decision trees for classification

An algorithm for learning decision trees for classification and prediction is described which converts realvalued 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.