Ulrich Bodenhofer, Mario Drobics, Erich Klement,
"FS-FOIL: An Inductive Learning Method for Extracting Interpretable Fuzzy Descriptions"
, in International Journal of Approximate Reasoning, Vol. 32, Nummer 2-3, Seite(n) 131-152, 2-2003
Original Titel:
FS-FOIL: An Inductive Learning Method for Extracting Interpretable Fuzzy Descriptions
Sprache des Titels:
Englisch
Original Kurzfassung:
This paper is concerned with FS-FOIL - an extension of Quinlan's First-Order Inductive Learning
Method (FOIL). In contrast to the classical FOIL algorithm, FS-FOIL uses fuzzy predicates and,
thereby, allows to deal not only with categorical variables, but also with numerical ones, without
the need to draw sharp boundaries. This method is described in full detail along with discussions
how it can be applied in different traditional application scenarios - classification, fuzzy
modeling, and clustering. We provide examples of all three types of applications in order to
illustrate the efficiency, robustness, and wide applicability of the FS-FOIL method.
Sprache der Kurzfassung:
Englisch
Englischer Titel:
FS-FOIL: An Inductive Learning Method for Extracting Interpretable Fuzzy Descriptions
Englische Kurzfassung:
This paper is concerned with FS-FOIL - an extension of Quinlan's First-Order Inductive Learning
Method (FOIL). In contrast to the classical FOIL algorithm, FS-FOIL uses fuzzy predicates and,
thereby, allows to deal not only with categorical variables, but also with numerical ones, without
the need to draw sharp boundaries. This method is described in full detail along with discussions
how it can be applied in different traditional application scenarios - classification, fuzzy
modeling, and clustering. We provide examples of all three types of applications in order to
illustrate the efficiency, robustness, and wide applicability of the FS-FOIL method.
Forschungs-