GauNBFuzzyParam {FuzzyClass}

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

train

matrix or data frame of training set cases.

cl

factor of true classifications of training set

alphacut

value of the alpha-cut parameter, this value is between 0 and 1.

metd

Method of transforming the triangle into scalar, It is the type of data entry for the test sample, use metd 1 if you want to use the Yager technique, metd 2 if you want to use the Q technique of the uniformity test (article: Directional Statistics and Shape analysis), and metd 3 if you want to use the Thorani technique

cores

how many cores of the computer do you want to use to use for prediction (default = 2)

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])