RoughSets (version 1.0-0)

FS.greedy.heuristic.superreduct.RST: The greedy heuristic method for determining superreduct based on RST

Description

It is used to get the feature subset (superreduct) based on the greedy heuristic algorithm employing some quality measurements. The detailed description can be seen in FS.greedy.heuristic.reduct.RST.

Usage

FS.greedy.heuristic.superreduct.RST(decision.table,
    decisionIdx = ncol(decision.table), qualityF = X.gini,
    nAttrs = NULL, ...)

Arguments

decision.table

a "DecisionTable" class representing decision table. See SF.asDecisionTable.

decisionIdx

a integer value representing an index of decision attribute.

qualityF

a function calculating quality on a set of attributes.

See FS.greedy.heuristic.reduct.RST.

nAttrs

a vector representing indexes of conditional attributes.

...

other parameters.

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"greedy.heuristic.superreduct".
  • 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"RST"which means rough set theory.

References

A. Janusz and S. Stawicki, "Applications of Approximate Reducts to the Feature Selection Problem", Proceedings of International Conference on Rough Sets and Knowledge Technology ({RSKT}), vol. 6954, p. 45 - 50 (2011).

D. Slezak, "Approximate Entropy Reducts", Fundamenta Informaticae, vol. 53, no. 3 - 4, p. 365 - 390 (2002).

J. Wroblewski, "Ensembles of Classifiers Based on Approximate Reducts", Fundamenta Informaticae, vol. 47, no. 3 - 4, p. 351 - 360 (2001).

See Also

FS.quickreduct.RST and FS.feature.subset.computation.

Examples

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

## evaluate single reduct
res.1 <- FS.greedy.heuristic.superreduct.RST(decision.table, qualityF = X.nOfConflictsSqrt)

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