Fuzzy Unordered Rule Induction Algorithm (FURIA)

A Fuzzy Unordered Rule Induction Algorithm (FURIA) is a Rule Induction Algorithm that can learn fuzzy and unordered rule sets.

• Context:
  • It was initially develped by Huhn & Hullermeier (2009).
  • It is an extension of the RIPPER algorithm.
  • It can be Implemented by a FURIA System to solve a FURIA Task.
  • …
• Example(s):
  • the original FURIA Algorithm proposed by Huhn & Hullermeier (2009).
  • …
• Counter-Example(s):
  • CN2 Algorithm,
  • FINDBESTRULE Algorithm.
  • FOIL Algorithm,
  • LEM-based Algorithms,
  • PROGOL Algorithm,
  • RIPPER Algorithm,
  • Rulearner Algorithm,
  • RULEX Algorithm.
• See: Pattern Mining Algorithm, Decision Tree Induction Algorithm, Inductive Logic Programming, If-Then Rule, Fuzzy Logic.

---

---

References

2009

• (Huhn & Hullermeier, 2009) ⇒ Jens Huhn, and Eyke Hullermeier. (2009). “FURIA: An Algorithm for Unordered Fuzzy Rule Induction.” In: Data mining and knowledge discovery Journal, 19(3). doi:10.1007/s10618-009-0131-8
  • QUOTE: 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.

---