Title:	Fuzzy and Non-Fuzzy Classifiers
Version:	0.1.6
Description:	It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) <doi:10.1007/s40815-020-00936-4>.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
Imports:	caTools, doParallel, e1071, EnvStats, foreach, MASS, mvtnorm, parallel, purrr, Rdpack, rootSolve, stats, trapezoid
RdMacros:	Rdpack
Depends:	R (≥ 2.10)
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, maxLik, dplyr
VignetteBuilder:	knitr
Config/testthat/edition:	3
URL:	https://github.com/leapigufpb/FuzzyClass
BugReports:	https://github.com/leapigufpb/FuzzyClass/issues
Language:	en-US
NeedsCompilation:	no
Packaged:	2024-02-14 14:05:53 UTC; j0d4
Author:	Jodavid Ferreira [aut, cre], Ronei Moraes [ctb], Arthur Ricardo [ctb]
Maintainer:	Jodavid Ferreira <jodavid@protonmail.com>
Repository:	CRAN
Date/Publication:	2024-02-14 17:50:02 UTC

FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Description

It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) doi:10.1007/s40815-020-00936-4.

Author(s)

Maintainer: Jodavid Ferreira jodavid@protonmail.com (ORCID)

Other contributors:

• Ronei Moraes ronei@de.ufpb.br (ORCID) [contributor]

• Arthur Ricardo arthur.ricardo17@gmail.com [contributor]

See Also

Useful links:

• https://github.com/leapigufpb/FuzzyClass

• Report bugs at https://github.com/leapigufpb/FuzzyClass/issues

Double Weighted Fuzzy Gamma Naive Bayes

Description

DWFuzzyGammaNaiveBayes Double Weighted Fuzzy Gamma Naive Bayes

Usage

DWFuzzyGammaNaiveBayes(train, cl, cores = 2, fuzzy = TRUE, wdelta, weta)

Arguments

Value

A vector of classifications

References

Moraes RM, Soares EAMG, Machado LS (2020). “A double weighted fuzzy gamma naive bayes classifier.” Journal of Intelligent & Fuzzy Systems, 38(1), 577–588.

Examples

set.seed(1) # determining a seed
data(GamWeightData)

# Splitting into Training and Testing
split <- caTools::sample.split(t(GamWeightData[, 1]), SplitRatio = 0.7)
Train <- subset(GamWeightData, split == "TRUE")
Test <- subset(GamWeightData, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_NBT <- DWFuzzyGammaNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2,
  wdelta = c(2.002/6,1.998/6,2.000/6),
  weta = c(3/10,2/10, 5/10)
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 4])

Fuzzy Exponential Naive Bayes Classifier with Fuzzy parameters

Description

ExpNBFuzzyParam Fuzzy Exponential Naive Bayes Classifier with Fuzzy parameters

Usage

ExpNBFuzzyParam(train, cl, alphacut = 1e-04, metd = 2, cores = 2)

Arguments

Value

A vector of classifications

References

Rodrigues AK, Batista TV, Moraes RM, Machado LS (2016). “A new exponential naive bayes classifier with fuzzy parameters.” In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1188–1194. IEEE.

Examples

set.seed(1) # determining a seed
data(VirtualRealityData)

# Splitting into Training and Testing
split <- caTools::sample.split(t(VirtualRealityData[, 1]), SplitRatio = 0.7)
Train <- subset(VirtualRealityData, split == "TRUE")
Test <- subset(VirtualRealityData, split == "FALSE")

# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_FENB <- ExpNBFuzzyParam(
  train = Train[, -4],
  cl = Train[, 4], metd = 1, cores = 2
)

pred_FENB <- predict(fit_FENB, test)

head(pred_FENB)
head(Test[, 4])

Fuzzy Bayes Rule

Description

FuzzyBayesRule Fuzzy Bayes Rule

Usage

FuzzyBayesRule(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes R, Machado L (2008). “Fuzzy Bayes Rule for On-Line Training Assessment in Virtual Reality Simulators.” Multiple-Valued Logic and Soft Computing, 14, 325-338.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_NBT <- FuzzyBayesRule(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 5])

Fuzzy Beta Naive Bayes

Description

FuzzyBetaNaiveBayes Fuzzy Beta Naive Bayes

Usage

FuzzyBetaNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Rodrigues AKG, Soares EAMG, Machado LS (2020). “A new fuzzy beta naive Bayes classifier.” In Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020), 437–445. World Scientific.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
#----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_NBT <- FuzzyBetaNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 5])

Fuzzy Binomial Naive Bayes

Description

FuzzyBinomialNaiveBayes Fuzzy Binomial Naive Bayes

Usage

FuzzyBinomialNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Machado LS (2016). “A Fuzzy Binomial Naive Bayes classifier for epidemiological data.” In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 745–750. IEEE.

Examples

set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rbinom(100,size = 10, prob = 0.2),
                     vari2 = rbinom(100,size = 10, prob = 0.2),
                    vari3 = rbinom(100,size = 10, prob = 0.2), class = 1)
class2 <- data.frame(vari1 = rbinom(100,size = 10, prob = 0.5),
                     vari2 = rbinom(100,size = 10, prob = 0.5),
                    vari3 = rbinom(100,size = 10, prob = 0.5), class = 2)
class3 <- data.frame(vari1 = rbinom(100,size = 10, prob = 0.8),
                     vari2 = rbinom(100,size = 10, prob = 0.8),
                     vari3 = rbinom(100,size = 10, prob = 0.8), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_NBT <- FuzzyBinomialNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 4])

Fuzzy Exponential Naive Bayes

Description

FuzzyExponentialNaiveBayes Fuzzy Exponential Naive Bayes

Usage

FuzzyExponentialNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Machado LS (2016). “A fuzzy exponential naive bayes classifier.” In Uncertainty Modelling in Knowledge Engineering and Decision Making: Proceedings of the 12th International FLINS Conference, 207–212. World Scientific.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_NBT <- FuzzyExponentialNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 5])

Fuzzy Gamma Naive Bayes

Description

FuzzyGammaNaiveBayes Fuzzy Gamma Naive Bayes

Usage

FuzzyGammaNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Soares EAMG, Machado LS (2018). “A Fuzzy Gamma Naive Bayes Classifier.” In Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018), 691–699. World Scientific.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_NBT <- FuzzyGammaNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 5])

Fuzzy Gaussian Naive Bayes Classifier Zadeh-based

Description

FuzzyGaussianNaiveBayes Fuzzy Gaussian Naive Bayes Classifier Zadeh-based

Usage

FuzzyGaussianNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Machado LS (2012). “Online Assessment in Medical Simulators Based on Virtual Reality Using Fuzzy Gaussian Naive Bayes.” Journal of Multiple-Valued Logic & Soft Computing, 18.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_GNB <- FuzzyGaussianNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_GNB <- predict(fit_GNB, test)

head(pred_GNB)
head(Test[, 5])

Fuzzy Naive Bayes Geometric Classifier

Description

FuzzyGeoNaiveBayes Naive Bayes Geometric Classifier

Usage

FuzzyGeoNaiveBayes(train, cl, cores = 2, fuzzy = T)

Arguments

Value

A vector of classifications

References

Ferreira J, Lopes A, Machado L, Moraes R (2023). “A Novel Fuzzy Geometric Naive Bayes Network for Online Skills Assessment in Training Based on Virtual Reality.” In Proceedings of the 15th International Joint Conference on Computational Intelligence, 395–401.

Examples

set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rgeom(100,prob = 0.2),
                     vari2 = rgeom(100,prob = 0.2),
                     vari3 = rgeom(100,prob = 0.2), class = 1)
class2 <- data.frame(vari1 = rgeom(100,prob = 0.5),
                     vari2 = rgeom(100,prob = 0.5),
                     vari3 = rgeom(100,prob = 0.5), class = 2)
class3 <- data.frame(vari1 = rgeom(100,prob = 0.9),
                     vari2 = rgeom(100,prob = 0.9),
                     vari3 = rgeom(100,prob = 0.9), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_NBT <- FuzzyGeoNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 4])

Fuzzy Naive Bayes

Description

FuzzyNaiveBayes Fuzzy Naive Bayes

Usage

FuzzyNaiveBayes(train, cl, fuzzy = TRUE, m = NULL, Pi = NULL)

Arguments

Value

A vector of classifications

References

Moraes RM, Machado LS (2009). “Another approach for fuzzy naive bayes applied on online training assessment in virtual reality simulators.” In Proceedings of Safety Health and Environmental World Congress, 62–66.

Examples

# Example Fuzzy with Discrete Features
set.seed(1) # determining a seed
data(HouseVotes84)

# Splitting into Training and Testing
split <- caTools::sample.split(t(HouseVotes84[, 1]), SplitRatio = 0.7)
Train <- subset(HouseVotes84, split == "TRUE")
Test <- subset(HouseVotes84, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -1]
fit_FNB <- FuzzyNaiveBayes(
  train = Train[, -1],
  cl = Train[, 1]
)

pred_FNB <- predict(fit_FNB, test)

head(pred_FNB)
head(Test[, 1])


# Example Fuzzy with Continuous Features
set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_FNB <- FuzzyNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5]
)

pred_FNB <- predict(fit_FNB, test)

head(pred_FNB)
head(Test[, 5])

Fuzzy Poisson Naive Bayes

Description

FuzzyPoissonNaiveBayes Fuzzy Poisson Naive Bayes

Usage

FuzzyPoissonNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Machado LS (2015). “A fuzzy poisson naive bayes classifier for epidemiological purposes.” In 2015 7th International Joint Conference on Computational Intelligence (IJCCI), volume 2, 193–198. IEEE.

Examples

set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rpois(100,lambda = 2),
                     vari2 = rpois(100,lambda = 2),
                     vari3 = rpois(100,lambda = 2), class = 1)
class2 <- data.frame(vari1 = rpois(100,lambda = 1),
                     vari2 = rpois(100,lambda = 1),
                     vari3 = rpois(100,lambda = 1), class = 2)
class3 <- data.frame(vari1 = rpois(100,lambda = 5),
                     vari2 = rpois(100,lambda = 5),
                     vari3 = rpois(100,lambda = 5), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_NBT <- FuzzyPoissonNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 4])

Fuzzy Naive Bayes Trapezoidal Classifier

Description

FuzzyTrapezoidalNaiveBayes Fuzzy Naive Bayes Trapezoidal Classifier

Usage

FuzzyTrapezoidalNaiveBayes(train, cl, cores = 2, fuzzy = T)

Arguments

Value

A vector of classifications

References

Lopes A, Ferreira J, Machado LS, Moraes RM (2022). “A New Fuzzy Trapezoidal Naive Bayes Network as basis for Assessment in Training based on Virtual Reality.” In The 15th International FLINS Conference on Machine learning, Multi agent and Cyber physical systems (FLINS 2022). Nankai University.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_NBT <- FuzzyTrapezoidalNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 5])

Fuzzy Naive Bayes Triangular Classifier

Description

FuzzyTriangularNaiveBayes Fuzzy Naive Bayes Triangular Classifier

Usage

FuzzyTriangularNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

Value

A vector of classifications

References

Moraes RM, Silva ILA, Machado LS (2020). “Online skills assessment in training based on virtual reality using a novel fuzzy triangular naive Bayes network.” In Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020), 446–454. World Scientific.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_NBT <- FuzzyTriangularNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 5])

Gamma Weighted Data

Description

A dataset simulated containing training data from a Gamma Distribution

Usage

GamWeightData

Format

A dataset with 600 rows and 4 variables with 1 label.

Fuzzy Gaussian Naive Bayes Classifier with Fuzzy parameters

Description

GauNBFuzzyParam Fuzzy Gaussian Naive Bayes Classifier with Fuzzy parameters

Usage

GauNBFuzzyParam(train, cl, alphacut = 1e-04, metd = 2, cores = 2)

Arguments

Value

A vector of classifications

References

Moraes RM, Ferreira JA, Machado LS (2021). “A New Bayesian Network Based on Gaussian Naive Bayes with Fuzzy Parameters for Training Assessment in Virtual Simulators.” International Journal of Fuzzy Systems, 23(3), 849–861.

Examples

set.seed(1) # determining a seed
data(iris)

# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -5]
fit_FGNB <- GauNBFuzzyParam(
  train = Train[, -5],
  cl = Train[, 5], metd = 1, cores = 2
)

pred_FGNB <- predict(fit_FGNB, test)

head(pred_FGNB)
head(Test[, 5])

United States Congressional Voting Records 1984

Description

This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition).

Usage

data(HouseVotes84)

Format

A data frame with 435 observations on 17 variables:

Source

• Source: Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc., ington, D.C., 1985

• Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)

These data have been taken from the UCI Repository Of Machine Learning Databases at

• https://archive.ics.uci.edu/datasets

• https://archive.ics.uci.edu/datasets

and were converted to R format by Friedrich Leisch.

References

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [https://archive.ics.uci.edu/datasets]. Irvine, CA: University of California, Department of Information and Computer Science.

Examples

data(HouseVotes84)
summary(HouseVotes84)

Fuzzy Poisson Naive Bayes Classifier with Fuzzy parameters

Description

PoiNBFuzzyParam Fuzzy Poisson Naive Bayes Classifier with Fuzzy parameters

Usage

PoiNBFuzzyParam(train, cl, alphacut = 1e-04, metd = 2, cores = 2)

Arguments

Value

A vector of classifications

References

Soares E, Machado L, Moraes R (2016). “Assessment of poisson naive bayes classifier with fuzzy parameters using data from different statistical distributions.” In IV Bazilian Congress on Fuzzy Sistems (CBSF 2016), volume 1, 57–68.

Examples

set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rpois(100,lambda = 2),
                     vari2 = rpois(100,lambda = 2),
                     vari3 = rpois(100,lambda = 2), class = 1)
class2 <- data.frame(vari1 = rpois(100,lambda = 1),
                     vari2 = rpois(100,lambda = 1),
                     vari3 = rpois(100,lambda = 1), class = 2)
class3 <- data.frame(vari1 = rpois(100,lambda = 5),
                     vari2 = rpois(100,lambda = 5),
                     vari3 = rpois(100,lambda = 5), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_FPoiNB <- PoiNBFuzzyParam(
  train = Train[, -4],
  cl = Train[, 4], metd = 1, cores = 2
)

pred_FPoiNB <- predict(fit_FPoiNB, test)

head(pred_FPoiNB)
head(Test[, 4])

Fuzzy Rule-based System

Description

FuzzyRuleBasedSystem Fuzzy Rule-based System

Usage

Set.Universe(minX, maxX)

TriFS(U, a, b, c)

TraFS(U, a, b, c, d)

GauFS(U, m, s)

uniFS(U, FS, FS1, FS2)

compFS(U, FS)

dmFS(x, U, FS)

centroidFS(U, FS)

OR(FRBS, x, y)

AND(FRBS, x, y)

NOT(FRBS, x)

Implication(FRBS, x, U, FS)

Aggregation(FRBS, U, Lst)

plotFS(Universe, FS)

References

Moraes RM, Rodrigues AKG, Soares EAMG, Machado LS (2020). “A new fuzzy beta naive Bayes classifier.” In Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020), 437–445. World Scientific.

Examples

## Creating Fuzzy Variables and Fuzzy Partitions
## Example 1 - It is the same of the R-Package 'Sets'

## Creating Fuzzy Variables and Fuzzy Partitions
## Example is the same of the R-Package 'Sets'
## One Domain was used for Evaluation and Tip

## Universe X - evaluation by the consumer in [0,10]
minX = 0
maxX = 25
X <- Set.Universe(minX, maxX)
FRBS = "Mandani"

## Service
service.poor <- GauFS(X, 0.0, 1.5)
#plotFS(X,service.poor)

service.good <- GauFS(X, 5.0, 1.5)
#plotFS(X,service.good)

service.excelent <- GauFS(X, 10, 1.5)
#plotFS(X,service.excelent)

## Food
food.rancid <- TraFS(X, 0, 0, 2, 4)
#plotFS(X,food.rancid)

food.delicious <- TraFS(X, 7, 9, 10, 10)
#plotFS(X,food.delicious)

## Tip
tip.cheap <- TriFS(X, 0, 5, 10)
#plotFS(X,tip.cheap)

tip.average <- TriFS(X, 7.5, 12.5, 17.5)
#plotFS(X,tip.average)

tip.generous <- TriFS(X, 15, 20, 25)
#plotFS(X,tip.generous)

## Set the input
## Evaluation by the consumer (Facts)

service = 3
food = 8

## Rules -- Specify rules and add outputs in a list
## A list is created in order to store the output of each rule

## Out_Rules_Lst <- list()


## 1) IF service is poor OR food is rancid THEN tip is cheap
## antecedent of the rule
## temp <- NOT(FRBS, dmFS(service,X,service.poor))
temp <- OR(FRBS, dmFS(service,X,service.poor), dmFS(food,X,food.rancid))

# consequent of the rule
fr1 <- Implication(FRBS, temp, X, tip.cheap)
plotFS(X,fr1)


## 2) IF service is good THEN tip is average

## temp <- NOT(FRBS, dmFS(service,X,service.good))
temp <- dmFS(service,X,service.good)
fr2 <- Implication(FRBS, temp, X, tip.average)
plotFS(X,fr2)

## Cria lista com as saidas. Precisa de duas para dar certo
Out_Rules_Lst <- list(fr1,fr2)


## 3) IF service is excelent OR food is delicious THEN tip is generous

## temp <- NOT(OR(FRBS, dmFS(service,X,service.excelent), dmFS(food,X,food.delicious)))
temp <- OR(FRBS, dmFS(service,X,service.excelent), dmFS(food,X,food.delicious))
fr3 <- Implication(FRBS, temp, X, tip.generous)
plotFS(X,fr3)

## Cria uma nova lista com a saída. Agora é possível concatenar as listas
lst_aux <- list(fr3)

## Compute the agregation of all previous Rules
## Concatenando as listas
Out_Rules_Lst <- c(Out_Rules_Lst, lst_aux)

## Numero de regras eh o comprimento da lista
length(Out_Rules_Lst)

OutFS <-Aggregation(FRBS, X, Out_Rules_Lst)
plotFS(X,OutFS)


## Compute centroid in order provide final decision

FinalDecision <- centroidFS(X,OutFS)
FinalDecision

Simulated Data

Description

A dataset containing training data from Gammma Distribuition

Usage

SimulatedData

Format

A dataset with 600 rows and 4 variables with 1 label.

Virtual Reality Simulator Data

Description

A dataset containing training data from a virtual reality simulator

Usage

VirtualRealityData

Format

A dataset with 600 rows and 4 variables with 1 label.