Frbs: Fuzzy Rule-Based Systems for Classification and Regression in R
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Riza, Lala Septem; Bergmeir, Christoph Norbert; Herrera Triguero, Francisco; Benítez Sánchez, José ManuelEditorial
American Statistical Association
Materia
Fuzzy inference systems Soft computing Fuzzy sets Genetic fuzzy systems Fuzzy neural networks
Date
2015Referencia bibliográfica
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]
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)".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.
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