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frbs (version 3.2-0)

Fuzzy Rule-Based Systems for Classification and Regression Tasks

Description

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.

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Version

Install

install.packages('frbs')

Monthly Downloads

1,282

Version

3.2-0

License

GPL (>= 2) | file LICENSE

Last Published

December 15th, 2019

Functions in frbs (3.2-0)

GFS.GCCL

GFS.GCCL model building
GFS.Thrift.test

GFS.Thrift: The prediction phase
GFS.Thrift

GFS.Thrift model building
FRBCS.eng

FRBCS: prediction phase
HyFIS

HyFIS model building
FS.HGD

FS.HGD model building
HGD.update

FS.HGD updating function
GFS.GCCL.eng

GFS.GCCL.test: The prediction phase
ECM

Evolving Clustering Method
SLAVE.test

SLAVE.test: The prediction phase
GFS.FR.MOGUL

GFS.FR.MOGUL model building
GFS.FR.MOGUL.test

GFS.FR.MOGUL: The prediction phase
WM

WM model building
SBC.test

SBC prediction phase
SLAVE

SLAVE model building
GFS.LT.RS

GFS.LT.RS model building
data.gen3d

A data generator
frbs.gen

The frbs model generator
frbs.eng

The prediction phase
write.frbsPMML

The frbsPMML writer
defuzzifier

Defuzzifier to transform from linguistic terms to crisp values
plotMF

The plotting function
frbsPMML

The frbsPMML generator
frbsObjectFactory

The object factory for frbs objects
norm.data

The data normalization
frbsData

Data set of the package
frbs.learn

The frbs model building function
GFS.LT.RS.test

GFS.LT.RS: The prediction phase
rulebase

The rule checking function
SBC

The subtractive clustering and fuzzy c-means (SBC) model building
HyFIS.update

HyFIS updating function
summary.frbs

The summary function for frbs objects
frbs-package

Getting started with the frbs package
denorm.data

The data de-normalization
fuzzifier

Transforming from crisp set into linguistic terms
inference

The process of fuzzy reasoning
read.frbsPMML

The frbsPMML reader
predict.frbs

The frbs prediction stage
FIR.DM

FIR.DM model building
FH.GBML

FH.GBML model building
ANFIS

ANFIS model building
DM.update

FIR.DM updating function
DENFIS.eng

DENFIS prediction function
DENFIS

DENFIS model building
ANFIS.update

ANFIS updating function
FRBCS.CHI

FRBCS.CHI model building
FRBCS.W

FRBCS.W model building