Isotonic Classification Trees

Abstract. We propose a new algorithm for learning isotonic classification trees. It relabels non-monotone leaf nodes by performing the isotonic regression on the collection of leaf nodes. In case two leaf nodes with a common parent have the same class after relabeling, the tree is pruned in the parent node. Since we consider problems with ordered class labels, all results are evaluated on the basis of L1 prediction error. We experimentally compare the performance of the new algorithm with standard classification trees.

• Type: Journal
• Year: 2009
• Where: IDA
• Authors: Rémon van de Kamp, Ad Feelders, Nicola Barile
• Keywords: Classification Trees, IDA 2009, Information Technology, Isotonic Classification Trees, Leaf