R: The near-optimal reduction algorithm based on fuzzy rough set...

FS.nearOpt.fvprs.FRST {RoughSets} | R Documentation

## The near-optimal reduction algorithm based on fuzzy rough set theory

### Description

This is a function implementing the near-optimal reduction algorithm by employing fuzzy variable precision rough sets (FVPRS) for feature selection based on FRST proposed by (Zhao et al, 2009).

### Usage

    FS.nearOpt.fvprs.FRST(decision.table, alpha.precision = 0.05)

### Arguments

`decision.table` | an object of a `"DecisionTable"` class representing a decision table. See `SF.asDecisionTable`. In this case, the decision attribute must be nominal.
`alpha.precision` | a numeric value representing variable precision of FVPRS. See `BC.LU.approximation.FRST`.

### Details

The near-optimal algorithm is an algorithm to find one reduct only rather than all reducts. It modifies the α-reduction based on discernibility matrix by using a heuristic algorithm. To get basic knowledge about discernibility matrix, users can refers to the `"alpha.red"` discernibility type in `BC.discernibility.mat.FRST` .

It should be noted that this function does not give the new decision table directly. The other additional function called `SF.applyDecTable` is used to produce the new decision table based on information about the reduct from this function.

### Value

A class `"FeatureSubset"` that contains the following components:

- `reduct`: a list representing a single reduct. In this case, it could be a superreduct or just a subset of features.

- `type.method`: a string representing the type of method which is `"near.optimal.fvprs"`.

- `type.task`: a string showing the type of task which is `"feature selection"`.

- `model`: a string representing the type of model. In this case, it is `"FRST"` which means fuzzy rough set theory.

### Author(s)

Lala Septem Riza

### References

S. Zhao, E. C. C. Tsang, and D. Chen, "The Model of Fuzzy Variable Precision Rough Sets", IEEE Trans. on Fuzzy Systems, vol. 17, no. 2, p. 451 - 467 (2009).

### See Also

`BC.discernibility.mat.FRST`

### Examples

    #########################################################
    ## Example 1: Hiring dataset containing 8 objects with 5 attributes
    #########################################################
    ## Not run: data(RoughSetData)
    decision.table <- RoughSetData$hiring.dt

    ## get reduct as FeatureSubset class
    reduct.1 <- FS.nearOpt.fvprs.FRST(decision.table)

    ## get new decision table according to the reduct
    new.decTable <- SF.applyDecTable(decision.table, reduct.1)
    ## End(Not run)

    #########################################################
    ## Example 2: Pima dataset containing 7 objects with 9 attributes
    #########################################################
    data(RoughSetData)
    decision.table <- RoughSetData$pima7.dt

    ## get reduct
    reduct.2 <- FS.nearOpt.fvprs.FRST(decision.table)

    ## get new decision table according to the reduct
    new.decTable <- SF.applyDecTable(decision.table, reduct.2)

[Package RoughSets version 1.3-7 Index]