# demo/FS.QuickReduct.FRST.Ex5.R In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

##########################################################
## Example 1: Dataset containing nominal values on all attributes
##########################################################
library(RoughSets)
data(RoughSetData)
decision.table <- RoughSetData\$hiring.dt

########## 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 = "modified.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 = "modified.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 = "modified.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 = "modified.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 = "modified.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 = "modified.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 = "modified.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 = "modified.QR", control = control)

########## using fuzzy discernibility matrix ##############
control <- list(alpha = 1)
reduct.9 <- FS.quickreduct.FRST(decision.table, type.method = "fuzzy.discernibility",
type.QR = "modified.QR", control = control)

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RoughSets documentation built on May 29, 2017, 7:06 p.m.