Decision trees with optimal joint partitioning

Decision tree methods generally suppose that the number of categories of the attribute to be predicted is fixed. Breiman et al., with their Twoing criterion in CART, considered gathering the categories of the predicted attribute into two supermodalities. In this article, we propose an extension of this method. We try to merge the categories in an optimal unspecified number of supermodalities. Our method, called Arbogodaï, allows during tree growing for grouping categories of the target variable as well as categories of the predictive attributes. It handles both categorical and quantitative attributes. At the end, the user can choose to generate either a set of single rules or a set of multiconclusion rules that provide interval-like predictions. © 2005 Wiley Periodicals, Inc.