frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks

This package implements functionality and various algorithms to build and use fuzzy rule-based systems (FRBSs). FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. This package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

Version: 2.2-0
Suggests: class, e1071
Published: 2014-02-03
Author: Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and Jose Manuel Benitez
Maintainer: Christoph Bergmeir <c.bergmeir at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: MachineLearning
CRAN checks: frbs results


Reference manual: frbs.pdf
Package source: frbs_2.2-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: frbs_2.2-0.tgz, r-oldrel: frbs_2.2-0.tgz
OS X Mavericks binaries: r-release: frbs_2.2-0.tgz
Old sources: frbs archive

Reverse dependencies:

Reverse depends: fuzzyMM