FURIA: an algorithm for unordered fuzzy rule induction. 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.

References in zbMATH (referenced in 14 articles , 1 standard article )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Dasgupta, Abhijit; Nayak, Losiana; Das, Ritankar; Basu, Debasis; Chandra, Preetam; De, Rajat K.: Pattern and rule mining for identifying signatures of epileptic patients from clinical EEG data (2020)
  2. Dimuro, Graçaliz Pereira; Lucca, Giancarlo; Bedregal, Benjamín; Mesiar, Radko; Sanz, José Antonio; Lin, Chin-Teng; Bustince, Humberto: Generalized (C_F_1 F_2)-integrals: from Choquet-like aggregation to ordered directionally monotone functions (2020)
  3. Lucca, Giancarlo; Antonio Sanz, José; Dimuro, Graçaliz Pereira; Bedregal, Benjamín; Bustince, Humberto; Mesiar, Radko: (C_F)-integrals: a new family of pre-aggregation functions with application to fuzzy rule-based classification systems (2018)
  4. Pota, Marco; Esposito, Massimo; De Pietro, Giuseppe: Likelihood-fuzzy analysis: from data, through statistics, to interpretable fuzzy classifiers (2018)
  5. Derhami, Shahab; Smith, Alice E.: An integer programming approach for fuzzy rule-based classification systems (2017)
  6. Paternain, D.; Bustince, H.; Pagola, M.; Sussner, P.; Kolesárová, A.; Mesiar, R.: Capacities and overlap indexes with an application in fuzzy rule-based classification systems (2016)
  7. García, David; Gámez, Juan Carlos; González, Antonio; Pérez, Raúl: An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach (2015)
  8. Jiao, Lianmeng; Pan, Quan; Denoeux, Thierry; Liang, Yan; Feng, Xiaoxue: Belief rule-based classification system: extension of FRBCS in belief functions framework (2015)
  9. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers (2014)
  10. Otero, José; Sánchez, Luciano; Couso, Inés; Palacios, Ana: Bootstrap analysis of multiple repetitions of experiments using an interval-valued multiple comparison procedure (2014)
  11. Alvarez-Alvarez, Alberto; Alonso, Jose M.; Trivino, Gracian: Human activity recognition in indoor environments by means of fusing information extracted from intensity of WiFi signal and accelerations (2013) ioport
  12. Trawiński, Krzysztof; Alonso, Jose M.; Hernández, Noelia: A multiclassifier approach for topology-based WiFi indoor localization (2013) ioport
  13. Hühn, Jens Christian; Hüllermeier, Eyke: An analysis of the FURIA algorithm for fuzzy rule induction (2010)
  14. Hühn, Jens; Hüllermeier, Eyke: FURIA: an algorithm for unordered fuzzy rule induction (2009) ioport