FS.reduct.computation: The reduct computation methods based on RST and FRST
In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Description Usage Arguments Details Value Author(s) See Also Examples

This function is a wrapper for computing different types of decision reducts and approximate decision reducts.

1	FS.reduct.computation(decision.table, method = "greedy.heuristic", ...)

`decision.table`	an object of a `"DecisionTable"` class representing a decision table. See `SF.asDecisionTable`.
`method`	a character representing the type of computation method to use. See in Section `Details`.
`...`	other parameters. See the parameters of `FS.greedy.heuristic.reduct.RST`, `FS.DAAR.heuristic.RST`, `FS.nearOpt.fvprs.FRST` and `FS.permutation.heuristic.reduct.RST`.

The implemented methods include the following approaches:

"greedy.heuristic": a greedy heuristic method for computation of decision reducts (or approximate decision reducts) based on RST. See FS.greedy.heuristic.reduct.RST.
"DAAR.heuristic": Dynamically Adapted Approximate Reduct heuristic, which is a modification of the greedy heuristic with a random probe test to avoid inclusion of irrelevant attributes to the reduct. See FS.DAAR.heuristic.RST.
"nearOpt.fvprs": the near-optimal reduction algorithm based on FRST. See FS.nearOpt.fvprs.FRST.
"permutation.heuristic": a permutation-based elimination heuristic for computation of decision reducts based on RST. See FS.permutation.heuristic.reduct.RST.

Those methods can be selected by setting the parameter method. Additionally, SF.applyDecTable has been provided to generate a new decision table.

An object of a class "FeatureSubset". See FS.greedy.heuristic.reduct.RST, FS.DAAR.heuristic.RST, FS.permutation.heuristic.reduct.RST or FS.nearOpt.fvprs.FRST for more details.

Andrzej Janusz

D.discretization.RST, BC.LU.approximation.RST

##############################################################
## Example 1: generate reduct and new decision table
## using RST and FRST
##############################################################
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt

## generate a single reduct using RST
reduct.1 <- FS.reduct.computation(decision.table, method = "greedy.heuristic")

## generate a single reduct using FRST
reduct.2 <- FS.reduct.computation(decision.table, method = "nearOpt.fvprs")

## generate a new decision table using reduct.1
new.decTable.1 <- SF.applyDecTable(decision.table, reduct.1)

## generate new decision table using reduct.2
new.decTable.2 <- SF.applyDecTable(decision.table, reduct.2)