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

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 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. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

Version:	3.2-0
Suggests:	class, e1071, XML, R.rsp
Published:	2019-12-15
DOI:	10.32614/CRAN.package.frbs
Author:	Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, and Jose Manuel Benitez
Maintainer:	Christoph Bergmeir <c.bergmeir at decsai.ugr.es>
License:	GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL:	http://sci2s.ugr.es/dicits/software/FRBS
NeedsCompilation:	no
Citation:	frbs citation info
In views:	MachineLearning
CRAN checks:	frbs results

Documentation:

Reference manual:	frbs.pdf
Vignettes:	frbs: Fuzzy Rule-based Systems for Classification and Regression in R frbsPMML: A Universal Representation Framework for Fuzzy Rule-Based Systems Based on PMML

Downloads:

Package source:	frbs_3.2-0.tar.gz
Windows binaries:	r-devel: frbs_3.2-0.zip, r-release: frbs_3.2-0.zip, r-oldrel: frbs_3.2-0.zip
macOS binaries:	r-release (arm64): frbs_3.2-0.tgz, r-oldrel (arm64): frbs_3.2-0.tgz, r-release (x86_64): frbs_3.2-0.tgz, r-oldrel (x86_64): frbs_3.2-0.tgz
Old sources:	frbs archive

Reverse dependencies:

Reverse imports:	pheble
Reverse suggests:	flowml, mlr

Linking:

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