Data Mining and Knowledge Discovery

, Volume 19, Issue 3, pp 293–319 | Cite as

FURIA: an algorithm for unordered fuzzy rule induction

  • Jens Hühn
  • Eyke Hüllermeier


This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.


Classification Rule learning Fuzzy logic Rule stretching 


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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Mathematics and Computer SciencePhilipps-Universität MarburgMarburgGermany

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