Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/FeatureSelection.R
This is a function implementing the nearoptimal reduction algorithm by employing fuzzy variable precision rough sets (FVPRS) for feature selection based on FRST proposed by (Zhao et al, 2009).
1  FS.nearOpt.fvprs.FRST(decision.table, alpha.precision = 0.05)

decision.table 
an object of a 
alpha.precision 
a numeric value representing variable precision of FVPRS. See 
The nearoptimal algorithm is an algorithm to find one reduct only rather than all reducts. It modifies the αreduction based on
discernibility matrix by using a heuristic algorithm. To get basic knowledge about discernibility matrix,
users can refers to the "alpha.red"
discernibility type in BC.discernibility.mat.FRST
.
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 the new decision table based on
information about the reduct from this function.
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 "near.optimal.fvprs"
.
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 "FRST"
which means fuzzy rough set theory.
Lala Septem Riza
S. Zhao, E. C. C. Tsang, and D. Chen, "The Model of Fuzzy Variable Precision Rough Sets", IEEE Trans. on Fuzzy Systems, vol. 17, no. 2, p. 451  467 (2009).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  #########################################################
## Example 1: Hiring dataset containing 8 objects with 5 attributes
#########################################################
## Not run: data(RoughSetData)
decision.table < RoughSetData$hiring.dt
## get reduct as FeatureSubset class
reduct.1 < FS.nearOpt.fvprs.FRST(decision.table)
## get new decision table according to the reduct
new.decTable < SF.applyDecTable(decision.table, reduct.1)
## End(Not run)
#########################################################
## Example 2: Pima dataset containing 7 objects with 9 attributes
#########################################################
data(RoughSetData)
decision.table < RoughSetData$pima7.dt
## get reduct
reduct.2 < FS.nearOpt.fvprs.FRST(decision.table)
## get new decision table according to the reduct
new.decTable < SF.applyDecTable(decision.table, reduct.2)

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