R: Computing one reduct from a discernibility matrix

FS.one.reduct.computation {RoughSets} | R Documentation

## 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))

[Package RoughSets version 1.3-7 Index]