FuzzyBayesRule {FuzzyClass}

Fuzzy Bayes Rule

Description

FuzzyBayesRule Fuzzy Bayes Rule

Usage

FuzzyBayesRule(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 R, Machado L (2008). “Fuzzy Bayes Rule for On-Line Training Assessment in Virtual Reality Simulators.” Multiple-Valued Logic and Soft Computing, 14, 325-338.

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 <- FuzzyBayesRule(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

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