Fuzzy Exponential Naive Bayes Classifier with Fuzzy parameters

ExpNBFuzzyParam Fuzzy Exponential Naive Bayes Classifier with Fuzzy parameters

Usage

ExpNBFuzzyParam(train, cl, alphacut = 1e-04, metd = 2, cores = 2)

Arguments

train: matrix or data frame of training set cases
cl: factor of true classifications of training set
alphacut: value of the alpha-cut parameter, this value is between 0 and 1.
metd: Method of transforming the triangle into scalar, It is the type of data entry for the test sample, use metd 1 if you want to use the Yager technique, metd 2 if you want to use the Q technique of the uniformity test (article: Directional Statistics and Shape analysis), and metd 3 if you want to use the Thorani technique
cores: how many cores of the computer do you want to use to use for prediction (default = 2)

Value

A vector of classifications

References

Rodrigues AK, Batista TV, Moraes RM, Machado LS (2016). “A new exponential naive bayes classifier with fuzzy parameters.” In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1188--1194. IEEE.

Examples


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

# Splitting into Training and Testing
split <- caTools::sample.split(t(VirtualRealityData[, 1]), SplitRatio = 0.7)
Train <- subset(VirtualRealityData, split == "TRUE")
Test <- subset(VirtualRealityData, 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_FENB <- ExpNBFuzzyParam(
  train = Train[, -4],
  cl = Train[, 4], metd = 1, cores = 2
)

pred_FENB <- predict(fit_FENB, test)

head(pred_FENB)
#> [1] 1 1 1 1 1 1
#> Levels: 1 2 3
head(Test[, 4])
#> [1] 1 1 1 1 1 1