M. Drobics. Inductive learning of fuzzy regression trees. pages 16-21, 9, 2005.
|Buch||Proc. 4th Conf. of the European Society for Fuzzy Logic and Technology and 11 Recontres Francophones sur al Logique Flou et ses Applications (EUSFLAT/LFA 2005)|
||In this paper we present a novel approach to data-driven fuzzy modeling which aims to create highly accurate but also easily comprehensible models. This goal is obtained by defining a flexible but expressive language automatically from the data. This language is then used to inductively learn fuzzy regression trees from the data. Finally, we present a detailed comparison study on the performance of the proposed method and an outlook to future developments.