Hybrid fuzzyrough rule and induction and feature selection
Description
It is a function for generating rules based on hybrid fuzzyrough rule induction and feature selection. It allows for classification and regression tasks.
Usage
1  RI.hybridFS.FRST(decision.table, control = list())

Arguments
decision.table 
a 
control 
a list of other parameters which consist of

Details
It was proposed by (Jensen et al, 2009) attempting to combine rule induction and feature selection
at the same time. Basically this algorithm inserts some steps to generate rules
into the fuzzy QuickReduct algorithm (see FS.quickreduct.FRST
.
Furthermore, by introducing the degree of coverage, this algorithm selects proper rules.
This function allows not only for classification but also for regression problems. After obtaining the rules,
predicting can be done by calling predict
or predict.RuleSetFRST
.
Additionally, to get better representation we can execute summary
.
Value
A class "RuleSetFRST"
which has similar components as RI.GFRS.FRST
but in this case the threshold
component is not included.
Author(s)
Lala Septem Riza
References
R. Jensen, C. Cornelis, and Q. Shen, "Hybrid Fuzzyrough Rule Induction and Feature Selection", in: IEEE International Conference on Fuzzy Systems (FUZZIEEE), p. 1151  1156 (2009).
See Also
RI.indiscernibilityBasedRules.RST
, predict.RuleSetFRST
, and RI.GFRS.FRST
.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ###########################################################
## Example 1: Regression problem
###########################################################
data(RoughSetData)
decision.table < RoughSetData$housing7.dt
control < list(type.aggregation = c("t.tnorm", "lukasiewicz"), type.relation =
c("tolerance", "eq.3"), t.implicator = "lukasiewicz")
res.1 < RI.hybridFS.FRST(decision.table, control)
###########################################################
## Example 2: Classification problem
##############################################################
data(RoughSetData)
decision.table < RoughSetData$pima7.dt
control < list(type.aggregation = c("t.tnorm", "lukasiewicz"), type.relation =
c("tolerance", "eq.3"), t.implicator = "lukasiewicz")
res.2 < RI.hybridFS.FRST(decision.table, control)
