TY - JOUR
T1 - FS-FOIL
T2 - An inductive learning method for extracting interpretable fuzzy descriptions
AU - Drobics, Mario
AU - Bodenhofer, Ulrich
AU - Klement, Erich Peter
N1 - Funding Information:
This work has been done in the framework of the Kplus Competence Center Program which is funded by the Austrian Government, the Province of Upper Austria, and the Chamber of Commerce of Upper Austria.
PY - 2003/2
Y1 - 2003/2
N2 - 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.
AB - 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.
KW - Clustering
KW - Data mining
KW - Fuzzy rules
KW - Inductive learning
KW - Interpretability
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=0037311077&partnerID=8YFLogxK
U2 - 10.1016/S0888-613X(02)00080-4
DO - 10.1016/S0888-613X(02)00080-4
M3 - Article
AN - SCOPUS:0037311077
SN - 0888-613X
VL - 32
SP - 131
EP - 152
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
IS - 2-3
ER -