FS.one.reduct.computation: Computing one reduct from a discernibility matrix
In janusza/RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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

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

FS.one.reduct.computation(
  discernibilityMatrix,
  greedy = TRUE,
  sampSize = 5,
  power = 1
)

`discernibilityMatrix`	a `"DiscernibilityMatrix"` class representing the discernibility matrix of RST and FRST.
`greedy`	a boolean value indicating whether the greedy heuristic or a stochastic search should be used in computations.
`sampSize`	an integer indicating the sample size for the stochastic search heuristic.
`power`	a numeric representing a parameter of the stochastic search heuristic.

An object of a class "ReductSet".

Andrzej Janusz

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).

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

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