FS.reduct.computation {RoughSets}

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, FS.nearOpt.fvprs.FRST and FS.permutation.heuristic.reduct.RST.

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. 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.

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.

Author(s)

Andrzej Janusz

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)