Double Weighted Fuzzy Hipergeometric Naive Bayes

DWFuzzyHipergeometricNaiveBayes Double Weighted Fuzzy Hipergeometric Naive Bayes

Usage

DWFuzzyHipergeometricNaiveBayes(
  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 (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

ferreira2025newFuzzyClass

Examples

set.seed(1)

substituir_zero <- function(x) {
  if (x == 0) {
    while(1){
     new_valor <- rhyper(1,3,30,10)
      if(new_valor != 0){
        return(new_valor)
      }
    }
  } else {
    return(x)
  }
}

#Building dataframe
class1 <- data.frame(rhyper(72,3,30,10), 
                     sample(seq(1,5),72,replace = TRUE), class = "Bem treinado")
colnames(class1)[1] <- "sucessos"
class1$sucessos <- sapply(class1$sucessos, substituir_zero)
colnames(class1)[2] <- "tentativas"
colnames(class1)[3] <- "avaliação"
class2 <- data.frame(rhyper(72,3,17,12), 
                     sample(seq(4,7),72,replace = TRUE), class = "Mediano")
colnames(class2)[1] <- "sucessos"
#class2$sucessos <- ifelse(class2$sucessos == 0, 2, class2$sucessos)
colnames(class2)[2] <- "tentativas"
colnames(class2)[3] <- "avaliação"
class3 <- data.frame(rhyper(72,3,9,11),
                     sample(seq(7,10),72,replace = TRUE), class = "Precisa treinar")
colnames(class3)[1] <- "sucessos"
#class3$sucessos <- ifelse(class3$sucessos == 0, 3, class3$sucessos)
colnames(class3)[2] <- "tentativas"
colnames(class3)[3] <- "avaliação"
data <- rbind(class1,class2,class3)

# Weights
weta1 <- c(0.22, 0.33, 0.45)
wdelta1 <- c(0.33,0.33,0.33) 

#spliting dataframe
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")

test <- Test[, -3]
fit_NHT <- DWFuzzyHipergeometricNaiveBayes(
 train = Train[, -3],
 cl = Train[, 3], cores = 2,
 fuzzy = TRUE,
 wdelta = wdelta1,
 weta = weta1)
#> 2.535463
#> 3.704793
#> 5.163978

pred_NHT <- predict(fit_NHT, test)