Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/FeatureSelection.R
It is a function implementing the permutation heuristic approach based on RST.
1 2  FS.permutation.heuristic.reduct.RST(decision.table, permutation = NULL,
decisionIdx = ncol(decision.table))

decision.table 
an object of a 
permutation 
a logical value, an integer vector or 
decisionIdx 
an index of the decision attribute. The default value is the last column of a decision table. 
Basically there are two steps in this algorithm which are
generating feature subset as a superreduct: In this step, we choose a subset of attributes that
discern all object from different decision classes. It is done by adding consecutive attributes
in an order defined by a permutation of attribute indices. The permutation can be random
or it can be explicitly given (by the parameter permutation
).
iterative elimination of attributes from the set obtained in the previous step. It is done in the reverse order to that, defined by the permutation.
More details regarding this algorithm can be found in (Janusz and Slezak, 2012).
Additionally, SF.applyDecTable
has been provided to generate new decision table.
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 "permutation.heuristic"
.
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.
epsilon
: the approximation threshold.
Andrzej Janusz
A. Janusz and D. Ślęzak, "Utilization of Attribute Clustering Methods for Scalable Computation of Reducts from HighDimensional Data". Proceedings of Federated Conference on Computer Science and Information Systems  FedCSIS, p. 295  302 (2012).
Andrzej Janusz and Dominik Slezak. "Rough Set Methods for Attribute Clustering and Selection". Applied Artificial Intelligence, 28(3):220–242, 2014.
FS.quickreduct.RST
and FS.reduct.computation
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ###################################################
## Example 1: Generate reduct and new decision table
###################################################
data(RoughSetData)
decision.table < RoughSetData$hiring.dt
## generate single reduct
res.1 < FS.permutation.heuristic.reduct.RST(decision.table,
permutation = NULL,
decisionIdx = 5)
print(res.1)
res.2 < FS.permutation.heuristic.reduct.RST(decision.table,
permutation = 4:1,
decisionIdx = 5)
print(res.2)
## generate new decision table according to the reduct
new.decTable < SF.applyDecTable(decision.table, res.1)

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