Fuzzy Gaussian Naive Bayes Classifier Zadeh-based — FuzzyGaussianNaiveBayes • FuzzyClass

FuzzyGaussianNaiveBayes Fuzzy Gaussian Naive Bayes Classifier Zadeh-based

Usage

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

Arguments

train: matrix or data frame of training set cases.
cl: factor of true classifications of training set
cores: how many cores of the computer do you want to use to use for prediction (default = 2)
fuzzy: boolean variable to use the membership function

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)
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(Test[, 5])
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica