FuzzyGeoNaiveBayes {FuzzyClass}

Fuzzy Naive Bayes Geometric Classifier

Description

FuzzyGeoNaiveBayes Naive Bayes Geometric Classifier

Usage

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

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 (default = 2)

fuzzy

boolean variable to use the membership function

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])