Description Usage Arguments Value Author(s) References See Also Examples
View source: R/FeatureSelection.R
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
.
1 2 3 4 
decision.table 
an object of a 
attrDescriptions 
a list containing possible values of attributes (columns)
in 
decisionIdx 
a integer value representing an index of decision attribute. 
qualityF 
a function for calculating a quality of an attribute subset.
See 
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 
inconsistentDecisionTable 
logical indicating whether the decision table is suspected
to be inconsistent or 
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.
Andrzej Janusz
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).
FS.quickreduct.RST
and FS.feature.subset.computation
.
1 2 3 4 5 6 7 8 9 10 11 12 13  ###################################################
## 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)

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