RoughSets (version 1.2-1)

FS.reduct.computation: The reduct computation methods based on RST and FRST

Description

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

Usage

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

Arguments

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,

Value

• 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.

Details

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. SeeFS.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. SeeFS.DAAR.heuristic.RST.
• "nearOpt.fvprs": the near-optimal reduction algorithm based on FRST. SeeFS.nearOpt.fvprs.FRST.
• "permutation.heuristic": a permutation-based elimination heuristic for computation of decision reducts based on RST. SeeFS.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.

See Also

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

Examples

##############################################################
## 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)