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

In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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())

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.

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 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)

RoughSets documentation built on May 29, 2024, 7:34 a.m.