RoughSets (version 1.3-7)

RI.hybridFS.FRST: Hybrid fuzzy-rough rule and induction and feature selection

Description

It is a function for generating rules based on hybrid fuzzy-rough rule induction and feature selection. It allows for classification and regression tasks.

Usage

RI.hybridFS.FRST(decision.table, control = list())

Value

A class "RuleSetFRST" which has similar components as RI.GFRS.FRST

but in this case the threshold component is not included.

Arguments

decision.table

a "DecisionTable" class representing the decision table. See SF.asDecisionTable.

control

a list of other parameters which consist of

• type.aggregation a list representing the type of aggregation. The default value is type.aggregation = c("t.tnorm", "lukasiewicz").

  See BC.IND.relation.FRST.

• type.relation the type of indiscernibility relation. The default value is type.relation = c("tolerance", "eq.3"). See BC.IND.relation.FRST.

• t.implicator the type of implication function. The default value is "lukasiewicz". See BC.LU.approximation.FRST.

Author

Lala Septem Riza

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.

References

R. Jensen, C. Cornelis, and Q. Shen, "Hybrid Fuzzy-rough Rule Induction and Feature Selection", in: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), p. 1151 - 1156 (2009).

See Also

RI.indiscernibilityBasedRules.RST, predict.RuleSetFRST, and RI.GFRS.FRST.

Examples

###########################################################
## 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)