Fuzzy modeling with decision trees

Mario Drobics, Ulrich Bodenhofer

Research output: Contribution to journalConference articlepeer-review

14 Citations (Scopus)


Decision trees are a well-known and widely used method for classification problems. For handling numerical attributes or even for numerical prediction, traditional decision trees based on crisp predicates are not suitable. Through the usage of fuzzy predicates for different types of attributes, not only the expressive power of decision trees can be extended, but it also allows to create models for numerical attributes in a very natural manner. In this paper, we will present a logical foundation for inductive learning of fuzzy decision trees. We further show how fuzzy logical inference methods can be applied with fuzzy decision trees to provide continuous output. Extending the underlying logical language with ordering-based fuzzy predicates enables us to generate not only more compact, but also more accurate, decision trees. These explanations are complemented by remarks on how the obtained results can be interpreted and altered by the user, to provide a theoretically founded method for interactive data analysis.

Original languageEnglish
Pages (from-to)611-616
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2002
Externally publishedYes
Event2002 IEEE International Conference on Systems, Man and Cybernetics - Yasmine Hammamet, Tunisia
Duration: 6 Oct 20029 Oct 2002


  • Decision trees
  • Fuzzy modeling
  • Inductive learning
  • Interpretability
  • Machine learning


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