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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/39549

Title: Frbs: Fuzzy Rule-Based Systems for Classification and Regression in R
Authors: Riza, Lala Septem
Bergmeir, Christoph Norbert
Herrera, Francisco
Benítez Sánchez, José Manuel
Issue Date: 2015
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.
Sponsorship: This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) under Projects TIN2009-14575, TIN2011-28488, TIN2013-47210-P, and P10-TIC-06858. Bergmeir held a scholarship from the Spanish Ministry of Education (MEC) of the \Programa de Formación del Profesorado Universitario (FPU)".
Publisher: American Statistical Association
Keywords: Fuzzy inference systems
Soft computing
Fuzzy sets
Genetic fuzzy systems
Fuzzy neural networks
URI: http://hdl.handle.net/10481/39549
ISSN: 1548-7660
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Riza, L.S.; et al. Frbs: Fuzzy Rule-Based Systems for Classification and Regression in R. Journal of Statistical Software, 65(6): online (2015). [http://hdl.handle.net/10481/39549]
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