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Decision trees with optimal joint partitioning
Author(s) -
Zighed Djamel A.,
Ritschard Gilbert,
Erray Walid,
Scuturici VasileMarian
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20091
Subject(s) - categorical variable , merge (version control) , computer science , decision tree , extension (predicate logic) , data mining , set (abstract data type) , artificial intelligence , mathematics , machine learning , information retrieval , programming language
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. Int J Int Syst 20: 693–718, 2005.