RoughSets (version 1.3-7)

FS.one.reduct.computation: 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)

Value

An object of a class "ReductSet".

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.

Author

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))