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

Mario Drobics, Ulrich Bodenhofer, Erich Peter Klement

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)131-152
Number of pages22
JournalInternational Journal of Approximate Reasoning
Volume32
Issue number2-3
DOIs
Publication statusPublished - Feb 2003
Externally publishedYes

Keywords

  • Clustering
  • Data mining
  • Fuzzy rules
  • Inductive learning
  • Interpretability
  • Machine learning

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