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frbs: Fuzzy Rule-Based Systems for Classification and Regression in R

Author

Listed:
  • Riza, Lala Septem
  • Bergmeir, Christoph
  • Herrera, Francisco
  • Benítez, José M.

Abstract

Fuzzy rule-based systems (FRBSs) are a well-known method family within soft computing. 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 addition 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.

Suggested Citation

  • Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
    • Handle: RePEc:jss:jstsof:v:065:i06
    DOI: http://hdl.handle.net/10.18637/jss.v065.i06
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    References listed on IDEAS

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    1. Shang-Ming Zhou & Ronan A Lyons & Sinead Brophy & Mike B Gravenor, 2012. "Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-14, December.
    2. Meyer, David & Hornik, Kurt, 2009. "Generalized and Customizable Sets in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i02).
    3. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    4. Bergmeir, Christoph & Benítez, José M., 2012. "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i07).
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    Cited by:

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    2. Wanke, Peter & Azad, Abul Kalam & Emrouznejad, Ali, 2018. "Efficiency in BRICS banking under data vagueness: A two-stage fuzzy approach," Global Finance Journal, Elsevier, vol. 35(C), pages 58-71.
    3. Miqueias Lima Duarte & Tatiana Acácio Silva & Jocy Ana Paixão Sousa & Amazonino Lemos Castro & Roberto Wagner Lourenço, 2025. "Application of a hybrid fuzzy inference system to map the susceptibility to fires," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(1), pages 1117-1141, January.
    4. Ajanta Das & Anindita Desarkar, 2018. "Decision Tree-Based Analytics for Reducing Air Pollution," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.
    5. Wanke, Peter & Falcão, Bernardo Bastos, 2017. "Cargo allocation in Brazilian ports: An analysis through fuzzy logic and social networks," Journal of Transport Geography, Elsevier, vol. 60(C), pages 33-46.

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