Fuzzy Naive Bayes Triangular Classifier

FuzzyTriangularNaiveBayes Fuzzy Naive Bayes Triangular Classifier

Usage

FuzzyTriangularNaiveBayes(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, 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)
#> [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