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

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Authors Lala Septem Riza, Christoph Bergmeir, Francisco F. Herrera, José Manuel Benítez
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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.
Date of publication 2015
Code Programming Language R

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