Fuzzy Gaussian Naive Bayes Classifier with Fuzzy parameters

GauNBFuzzyParam Fuzzy Gaussian Naive Bayes Classifier with Fuzzy parameters

Usage

GauNBFuzzyParam(
  train,
  cl,
  alphacut = 1e-04,
  metd = 2,
  alp = c(0.35, 0.7, 0.86),
  w = c(0.1, 0.3, 0.6),
  cores = 2,
  verbose = fuzzy_verbose()
)

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), 'metd=3' if you want to use the Thorani technique, 'metd=4' if you want to use the alpha-order technique and 'metd=5' if you want to use the GTFN technique (article: A novel approach for arithmetic operations and ranking of generalized fuzzy numbers with application).

alp

When metd for 4, it is necessary to have alp which are alpha-cut defined

w

When metd for 4, it is necessary to have w which are alpha-cut weights defined

cores

kept for backward compatibility; prediction is vectorized and does not start parallel workers (default = 2)

verbose

logical; if TRUE (default, see fuzzy_verbose()) a message reports the training progress.

Value

A trained GauNBFuzzyParamModel R6 object (which inherits from FuzzyClassifier). Use predict(object, newdata, type = "class") or object$predict(newdata) to classify new observations; type = "prob" (or "matrix") returns the matrix of class probabilities.

References

Moraes RM, Ferreira JA, Machado LS (2021). “A New Bayesian Network Based on Gaussian Naive Bayes with Fuzzy Parameters for Training Assessment in Virtual Simulators.” International Journal of Fuzzy Systems, 23(3), 849–861.

See also

GauNBFuzzyParamModel for the R6 interface and fuzzy_naive_bayes() (engine "gaussian_fuzzy_param") for the tidymodels interface.

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_FGNB <- GauNBFuzzyParam(
  train = Train[, -5],
  cl = Train[, 5], metd = 1, cores = 2
)
#> ℹ Gaussian Naive Bayes with Fuzzy Parameters: 105 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [16ms]
#> 

pred_FGNB <- predict(fit_FGNB, test)

head(pred_FGNB)
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(Test[, 5])
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica