library(RoughSets)
#############################################################
## In this example, we are using Iris dataset.
## It should be noted that since values of decision attribute in string,
## so they should be transformed into numeric values using unclass()
#############################################################
data(iris)
set.seed(2)
irisShuffled <- iris[sample(nrow(iris)),]
irisShuffled[,5] <- unclass(irisShuffled[,5])
iris.training <- irisShuffled[1:105,]
real.iris <- matrix(irisShuffled[106:nrow(irisShuffled),5], ncol = 1)
colnames(iris.training) <- c("Sepal.Length", "Sepal.Width", "Petal.Length",
"Petal.Width", "Species")
decision.table <- SF.asDecisionTable(dataset = iris.training, decision.attr = 5, indx.nominal = c(5))
## define newdata
tst.iris <- SF.asDecisionTable(dataset = irisShuffled[106:nrow(irisShuffled),1:4])
## perform FRNN algorithm using lower/upper approximation: Implicator/tnorm based approach
control <- list(type.LU = "implicator.tnorm", k = 20, t.tnorm = "lukasiewicz",
type.relation = c("tolerance", "eq.1"), t.implicator = "lukasiewicz")
res.test.POSNN <- C.POSNN.FRST(decision.table = decision.table,
newdata = tst.iris, control = control)
err = 100*sum(real.iris!=res.test.POSNN)/nrow(real.iris)
print("The result: ")
print(res.test.POSNN)
print("POSNN: percentage Error on Iris: ")
print(err)
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