FS-LiRT - An inductive learning method for creating comprehensible fuzzy regression trees

Autoren Mario Drobics
TitelFS-LiRT - An inductive learning method for creating comprehensible fuzzy regression trees
TypTechnischer Bericht
NummerSCCH-TR-0507
OrtHagenberg, Austria
InstitutionSCCH
Jahr2005
SCCH ID#507
Abstract

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.