R: The reduct computation methods based on RST and FRST

FS.reduct.computation {RoughSets} | R Documentation

## 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)

[Package RoughSets version 1.3-7 Index]