FS.quickreduct.RST: QuickReduct algorithm based on RST
In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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

This is a function for implementing the QuickReduct algorithm for feature selection based on RST proposed by (Shen and Chouchoulas, 2000). The algorithm produces only one feature subset that could be a superreduct.

1	FS.quickreduct.RST(decision.table, control = list())

decision.table

an object of a "DecisionTable" class representing a decision table. See SF.asDecisionTable.

control

other parameters. It contains the following component:

randomize: it has a boolean value. For the detailed description, see in Section Details. The default value is FALSE.

This algorithm considers the dependency degree (see A.Introduction-RoughSets) of the addition of each attribute to the current reduct candidate. Then the best candidate will be chosen. This process continues until the dependency of the subset equals to the dependency of the full dataset.

Additionally, in control parameter, we provide one component which is randomize. It has a boolean value: TRUE or FALSE that means we want to perform quickreduct by evaluating attributes randomly or all attributes in decision table.

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 new decision table based on information about the reduct from this function.

A class "FeatureSubset" that contains the following components:

reduct: a list representing single reduct. In this case, it could be super reduct or just subset of feature.
type.method: a string representing a type of method which is "quickreduct".
type.task: a string showing type of task which is "feature selection".
model: a string representing a type of model. In this case, it is "RST" which means rough set theory.

Lala Septem Riza

Q. Shen and A. Chouchoulas, "A Modular Approach to Generating Fuzzy Rules with Reduced Attributes for the Monitoring of Complex Systems", Engineering Applications of Artificial Intelligence, vol. 13, p. 263 - 278 (2000).

FS.quickreduct.FRST

###################################################
## Example 1: Evaluate reduct and generate
##            new decision table
###################################################
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt

## evaluate single reduct
res.1 <- FS.quickreduct.RST(decision.table)

## generate new decision table according to the reduct
new.decTable <- SF.applyDecTable(decision.table, res.1)