View source: R/FeatureSelection.R
FS.all.reducts.computation | R Documentation |
A wrapper function used for generating all decision reducts of a decision system. The reducts
are obtained from a discernibility matrix which can be computed using methods based on RST
and FRST. Therefore, it should be noted that before calling the function, we need to
compute a discernibility matrix using BC.discernibility.mat.RST
or
BC.discernibility.mat.FRST
.
FS.all.reducts.computation(discernibilityMatrix)
discernibilityMatrix |
an |
An object of a class "ReductSet"
.
Andrzej Janusz
BC.discernibility.mat.RST
, BC.discernibility.mat.FRST
.
########################################################
## Example 1: Generate all reducts and
## a new decision table using RST
########################################################
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt
## build the decision-relation discernibility matrix
res.2 <- BC.discernibility.mat.RST(decision.table, range.object = NULL)
## generate all reducts
reduct <- FS.all.reducts.computation(res.2)
## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, reduct, control = list(indx.reduct = 1))
##############################################################
## Example 2: Generate all reducts and
## a new decision table using FRST
##############################################################
## Not run: data(RoughSetData)
decision.table <- RoughSetData$hiring.dt
## build the decision-relation discernibility matrix
control.1 <- list(type.relation = c("crisp"),
type.aggregation = c("crisp"),
t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.1 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
control = control.1)
## generate single reduct
reduct <- FS.all.reducts.computation(res.1)
## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, reduct, control = list(indx.reduct = 1))
## End(Not run)
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