DWFuzzyGammaNaiveBayes: Double Weighted Fuzzy Gamma Naive Bayes

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Double Weighted Fuzzy Gamma Naive Bayes

Description

DWFuzzyGammaNaiveBayes Double Weighted Fuzzy Gamma Naive Bayes

Usage

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

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

Value

A vector of classifications

References

\insertRef

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])

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