Isotonic Classification Trees

Authors:
Rémon Kamp;Ad Feelders;Nicola Barile
Affiliations:
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508TB;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508TB;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508TB
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

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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 L 1 prediction error. We experimentally compare the performance of the new algorithm with standard classification trees.