FuzzyNaiveBayes {FuzzyClass}

Fuzzy Naive Bayes

Description

FuzzyNaiveBayes Fuzzy Naive Bayes

Usage

FuzzyNaiveBayes(train, cl, fuzzy = TRUE, m = NULL, Pi = NULL)

Arguments

train

matrix or data frame of training set cases

cl

factor of true classifications of training set

fuzzy

boolean variable to use the membership function

m

is M/N, where M is the number of classes and N is the number of train lines

Pi

is 1/M, where M is the number of classes

Value

A vector of classifications

References

Moraes RM, Machado LS (2009). “Another approach for fuzzy naive bayes applied on online training assessment in virtual reality simulators.” In Proceedings of Safety Health and Environmental World Congress, 62–66.

Examples

# Example Fuzzy with Discrete Features
set.seed(1) # determining a seed
data(HouseVotes84)

# Splitting into Training and Testing
split <- caTools::sample.split(t(HouseVotes84[, 1]), SplitRatio = 0.7)
Train <- subset(HouseVotes84, split == "TRUE")
Test <- subset(HouseVotes84, 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[, -1]
fit_FNB <- FuzzyNaiveBayes(
  train = Train[, -1],
  cl = Train[, 1]
)

pred_FNB <- predict(fit_FNB, test)

head(pred_FNB)
head(Test[, 1])


# Example Fuzzy with Continuous Features
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_FNB <- FuzzyNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5]
)

pred_FNB <- predict(fit_FNB, test)

head(pred_FNB)
head(Test[, 5])