Last update: 19-05-2022
# Installation
install.packages("devtools")
devtools::install_github("Jodavid/FuzzyClass")# package import
library(FuzzyClass)library(FuzzyClass)
library(caret)
#> Carregando pacotes exigidos: lattice
#> Carregando pacotes exigidos: ggplot2
#' ---------------------------------------------
#' The following shows how the functions are used:
#' --------------
#' Reading a database:
#'
#' Actual training data:
data(VirtualRealityData)
VirtualRealityData <- as.data.frame(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")
# ----------------
test = Test[,-4]# --------------------------------------------------
# Fuzzy Gaussian Naive Bayes with Fuzzy Parameters
fit_FGNB <- GauNBFuzzyParam(train = Train[,-4],
cl = Train[,4], metd = 1, cores = 1)
print(fit_FGNB)
#>
#> Fuzzy Gaussian Naive Bayes Classifier for Discrete Predictors
#>
#> Variables:
#> [1] "V1" "V2" "V3"
#> Class:
#> [1] "1" "2" "3"
saida <- predict(fit_FGNB, test)
confusionMatrix(factor(Test[,4]), saida)
#> Confusion Matrix and Statistics
#>
#> Reference
#> Prediction 1 2 3
#> 1 42 10 0
#> 2 9 42 9
#> 3 3 10 55
#>
#> Overall Statistics
#>
#> Accuracy : 0.7722
#> 95% CI : (0.7039, 0.8313)
#> No Information Rate : 0.3556
#> P-Value [Acc > NIR] : <2e-16
#>
#> Kappa : 0.6571
#>
#> Mcnemar's Test P-Value : 0.3757
#>
#> Statistics by Class:
#>
#> Class: 1 Class: 2 Class: 3
#> Sensitivity 0.7778 0.6774 0.8594
#> Specificity 0.9206 0.8475 0.8879
#> Pos Pred Value 0.8077 0.7000 0.8088
#> Neg Pred Value 0.9062 0.8333 0.9196
#> Prevalence 0.3000 0.3444 0.3556
#> Detection Rate 0.2333 0.2333 0.3056
#> Detection Prevalence 0.2889 0.3333 0.3778
#> Balanced Accuracy 0.8492 0.7624 0.8737
saida <- predict(fit_FGNB, test, type = "matrix")