Frbs: Fuzzy Rule-Based Systems for Classification and Regression in R
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AutorRiza, Lala Septem; Bergmeir, Christoph Norbert; Herrera, Francisco; Benítez Sánchez, José Manuel
American Statistical Association
Fuzzy inference systemsSoft computingFuzzy setsGenetic fuzzy systemsFuzzy neural networks
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]
PatrocinadorThis 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)".
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.
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