FuzzyNaiveBayes: Fuzzy Naive Bayes

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

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

\insertRef

moraes2009anotherFuzzyClass

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])

FuzzyClass documentation built on May 29, 2024, 8:37 a.m.