FS.greedy.heuristic.superreduct.RST {RoughSets}

The greedy heuristic method for determining superreduct based on RST

Description

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

Usage

FS.greedy.heuristic.superreduct.RST(
  decision.table,
  attrDescriptions = attr(decision.table, "desc.attrs"),
  decisionIdx = attr(decision.table, "decision.attr"),
  qualityF = X.gini,
  nAttrs = NULL,
  inconsistentDecisionTable = NULL
)

Arguments

decision.table

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

attrDescriptions

a list containing possible values of attributes (columns) in decision.table. It usually corresponds to attr(decision.table, "desc.attrs").

decisionIdx

a integer value representing an index of decision attribute.

qualityF

a function for calculating a quality of an attribute subset. See FS.greedy.heuristic.reduct.RST.

nAttrs

an integer between 1 and the number of conditional attributes. It indicates the attribute sample size for the Monte Carlo selection of candidating attributes. If set to NULL (default) all attributes are used and the algorithm changes to a standard greedy method for computation of decision reducts.

inconsistentDecisionTable

logical indicating whether the decision table is suspected to be inconsistent or NULL (the default) which indicated that a test should be made to determine the data consistency.

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.

Author(s)

Andrzej Janusz

References

Andrzej Janusz and Dominik Slezak. "Rough Set Methods for Attribute Clustering and Selection". Applied Artificial Intelligence, 28(3):220–242, 2014.

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. Ślęzak, "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.nOfConflicts)
print(res.1)

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