FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptions

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 t...

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Bibliographic Details
Published in:International journal of approximate reasoning 2003-02, Vol.32 (2), p.131-152
Main Authors: Drobics, Mario, Bodenhofer, Ulrich, Peter Klement, Erich
Format: Article
Language:English
Subjects:
Clustering
Data mining
Fuzzy rules
Inductive learning
Interpretability
Machine learning
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Summary: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.
ISSN:0888-613X
1873-4731
DOI:10.1016/S0888-613X(02)00080-4