FS-LiRT - An inductive learning method for creating comprehensible fuzzy regression trees
|Autoren|| Mario Drobics|
|Titel||FS-LiRT - An inductive learning method for creating comprehensible fuzzy regression trees|
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. The paper is closed with a detailed comparison study on the performance of the proposed method and an outlook to future developments.