library(RoughSets)
decision.table <- data.frame(c(-0.4, -0.4, -0.3, 0.3, 0.2, 0.2),
c(-0.3, 0.2, -0.4, -0.3, -0.3, 0),
c(-0.5, -0.1, -0.3, 0, 0, 0),
c("no", "yes", "no", "yes", "yes", "no"))
colnames(decision.table) <- c("a", "b", "c", "d")
decision.table <- SF.asDecisionTable(dataset = decision.table, decision.attr = 4, indx.nominal = c(4))
########## using fuzzy lower approximation ##############
control <- list(t.implicator = "lukasiewicz", type.relation = c("tolerance", "eq.1"), type.aggregation = c("t.tnorm", "lukasiewicz"))
reduct.1 <- FS.quickreduct.FRST(decision.table, type.method = "fuzzy.dependency",
type.QR = "fuzzy.QR", control = control)
########## using fuzzy boundary region ##############
control <- list(t.implicator = "lukasiewicz", type.relation = c("tolerance", "eq.1"), type.aggregation = c("t.tnorm", "lukasiewicz"))
reduct.2 <- FS.quickreduct.FRST(decision.table, type.method = "fuzzy.boundary.reg",
type.QR = "fuzzy.QR", control = control)
########## using vaquely quantified rough sets (VQRS) #########
control <- list(alpha = 0.9, q.some = c(0.1, 0.6), q.most = c(0.2, 1), type.aggregation = c("t.tnorm", "lukasiewicz"))
reduct.3 <- FS.quickreduct.FRST(decision.table, type.method = "vqrs",
type.QR = "fuzzy.QR", control = control)
########## ordered weighted average (OWA) #########
control <- list(t.implicator = "lukasiewicz", type.relation = c("tolerance", "eq.1"), m.owa = 3, type.aggregation = c("t.tnorm","lukasiewicz"))
reduct.4 <- FS.quickreduct.FRST(decision.table, type.method = "owa",
type.QR = "fuzzy.QR", control = control)
########## robust fuzzy rough sets (RFRS) #########
control <- list(t.implicator = "lukasiewicz", type.relation = c("tolerance", "eq.1"), type.rfrs = "k.trimmed.min",
type.aggregation = c("t.tnorm", "lukasiewicz"), k.rfrs = 0)
reduct.5 <- FS.quickreduct.FRST(decision.table, type.method = "rfrs",
type.QR = "fuzzy.QR", control = control)
########## using min positive region (delta) ###########
control <- list(alpha = 1, t.implicator = "lukasiewicz", type.relation = c("tolerance", "eq.1"), type.aggregation = c("t.tnorm", "lukasiewicz"))
reduct.6 <- FS.quickreduct.FRST(decision.table, type.method = "min.positive.reg",
type.QR = "fuzzy.QR", control = control)
########## using FVPRS approximation ##############
control <- list(alpha.precision = 0.05, t.implicator = "lukasiewicz", type.aggregation = c("t.tnorm", "lukasiewicz"),
type.relation = c("tolerance", "eq.1"))
reduct.7 <- FS.quickreduct.FRST(decision.table, type.method = "fvprs",
type.QR = "fuzzy.QR", control = control)
########## using beta.PFRS approximation ##############
control <- list(t.implicator = "lukasiewicz", type.relation = c("tolerance", "eq.1"), beta.quasi = 0.05,
type.aggregation = c("t.tnorm", "lukasiewicz"))
reduct.8 <- FS.quickreduct.FRST(decision.table, type.method = "beta.pfrs",
type.QR = "fuzzy.QR", control = control)
########## using fuzzy discernibility matrix ##############
control <- list(alpha = 1)
reduct.9 <- FS.quickreduct.FRST(decision.table, type.method = "fuzzy.discernibility",
type.QR = "fuzzy.QR", control = control)
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