frbs: fuzzy rule-based systems for classication and regression in R

Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, Jose Manuel Benitez

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Fuzzy rule-based systems (FRBSs) are a well-known method family within soft com- puting. They are based on fuzzy concepts to address complex real-world problems. We present the R package frbs which implements the most widely used FRBS models, namely, Mamdani and Takagi Sugeno Kang (TSK) ones, as well as some common variants. In ad- dition a host of learning methods for FRBSs, where the models are constructed from data, are implemented. In this way, accurate and interpretable systems can be built for data analysis and modeling tasks. In this paper, we also provide some examples on the usage of the package and a comparison with other common classification and regression methods available in R.
Original languageEnglish
Pages (from-to)1-30
Number of pages30
JournalJournal of Statistical Software
Volume65
Issue number6
DOIs
Publication statusPublished - May 2015
Externally publishedYes

Keywords

  • fuzzy inference systems
  • soft computing
  • fuzzy sets
  • genetic fuzzy systems
  • fuzzy neural networks

Cite this

Riza, Lala Septem ; Bergmeir, Christoph ; Herrera, Francisco ; Benitez, Jose Manuel. / frbs : fuzzy rule-based systems for classication and regression in R. In: Journal of Statistical Software. 2015 ; Vol. 65, No. 6. pp. 1-30.
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frbs : fuzzy rule-based systems for classication and regression in R. / Riza, Lala Septem; Bergmeir, Christoph ; Herrera, Francisco; Benitez, Jose Manuel.

In: Journal of Statistical Software, Vol. 65, No. 6, 05.2015, p. 1-30.

Research output: Contribution to journalArticleResearchpeer-review

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