FS.one.reduct.computation {RoughSets}

Computing one reduct from a discernibility matrix

Description

It is a function for computing one reduct from a discernibility matrix - it can use the greedy heuristic or a randomized (Monte Carlo) search.

Usage

FS.one.reduct.computation(discernibilityMatrix, greedy = TRUE, power = 1)

Arguments

discernibilityMatrix

a "DiscernibilityMatrix" class representing the discernibility matrix of RST and FRST.

greedy

a boolean value indicating whether the greedy heuristic or a randomized search should be used in computations.

power

a numeric representing a parameter of the randomized search heuristic.

Value

An object of a class "ReductSet".

Author(s)

Andrzej Janusz

References

Jan G. Bazan, Hung Son Nguyen, Sinh Hoa Nguyen, Piotr Synak, and Jakub Wroblewski, "Rough Set Algorithms in Classification Problem", Chapter 2 In: L. Polkowski, S. Tsumoto and T.Y. Lin (eds.): Rough Set Methods and Applications Physica-Verlag, Heidelberg, New York, p. 49 - 88 ( 2000).

See Also

BC.discernibility.mat.RST and BC.discernibility.mat.FRST.

Examples

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

## build the decision-relation discernibility matrix
res.1 <- BC.discernibility.mat.RST(decision.table)

## generate all reducts
reduct <- FS.one.reduct.computation(res.1)

## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, reduct, control = list(indx.reduct = 1))

##############################################################
## Example 2: Generate one reduct and
##            a new decision table using FRST
##############################################################
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt

## build the decision-relative discernibility matrix
control <- list(type.relation = c("crisp"),
                type.aggregation = c("crisp"),
                t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.2 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
                                    control = control)

## generate a single reduct
reduct <- FS.one.reduct.computation(res.2)

## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, reduct, control = list(indx.reduct = 1))