FuzzyClass (version 0.1.7)

DWFuzzyGammaNaiveBayes: Double Weighted Fuzzy Gamma Naive Bayes

Description

DWFuzzyGammaNaiveBayes Double Weighted Fuzzy Gamma Naive Bayes

Usage

DWFuzzyGammaNaiveBayes(train, cl, cores = 2, fuzzy = TRUE, wdelta, weta)

Value

A vector of classifications

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

wdelta

vector weight each class

weta

vector weight each feature

References

marcos2020doubleFuzzyClass

Examples

set.seed(1) # determining a seed
data(GamWeightData)

# Splitting into Training and Testing
split <- caTools::sample.split(t(GamWeightData[, 1]), SplitRatio = 0.7)
Train <- subset(GamWeightData, split == "TRUE")
Test <- subset(GamWeightData, 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 <- DWFuzzyGammaNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2,
  wdelta = c(2.002/6,1.998/6,2.000/6),
  weta = c(3/10,2/10, 5/10)
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 4])