FuzzyGeoNaiveBayes Naive Bayes Geometric Classifier
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
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)
#> [1] 2 1 2 2 1 1
#> Levels: 1 2 3
head(Test[, 4])
#> [1] 1 1 1 1 1 1