GauNBFuzzyParam: Fuzzy Gaussian Naive Bayes Classifier with Fuzzy parameters

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

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,
  alp = c(0.35, 0.7, 0.86),
  w = c(0.1, 0.3, 0.6),
  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), 'metd=3' if you want to use the Thorani technique, 'metd=4' if you want to use the alpha-order technique and 'metd=5' if you want to use the GTFN technique (article: A novel approach for arithmetic operations and ranking of generalized fuzzy numbers with application).
alp	When metd for 4, it is necessary to have alp which are alpha-cut defined
w	When metd for 4, it is necessary to have w which are alpha-cut weights defined
cores	kept for backward compatibility; prediction is vectorized and does not start parallel workers (default = 2)

Value

A vector of classifications

References

\insertRef

moraes2021newFuzzyClass

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])

FuzzyClass documentation built on Oct. 9, 2026, 5:07 p.m.