Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
frbs: Fuzzy Rule-Based Systems for Classification and Regression in R | Riza | Journal of Statistical Software
Authors: Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, José M. Benítez
Title: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R
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.

Page views:: 3667. Submitted: 2015-03-25. Published: 2015-06-01.
Paper: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R     Download PDF (Downloads: 12228)
Supplements:
frbs_3.1-0.tar.gz: R source package Download (Downloads: 246; 152KB)
v65i06.R: R example code from the paper Download (Downloads: 339; 838B)
v65i06-experiments.zip: Replication R code for experiments Download (Downloads: 257; 136KB)
v65i06-results.zip: Output files with results from experiments Download (Downloads: 238; 215KB)

DOI: 10.18637/jss.v065.i06

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.