RI.GFRS.FRST: Generalized fuzzy rough set rule induction based on FRST

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/RuleInduction.R

Description

It is a function generating rules in classification tasks using the fuzzy variable precision rough sets (FVPRS) approach (see BC.LU.approximation.FRST).

Usage

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RI.GFRS.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

  • alpha.precision: a numeric value representing variable precision of FVPRS. The default value is 0.05. See BC.LU.approximation.FRST.

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

    See BC.IND.relation.FRST.

  • type.relation: a type of indiscernibility relation. The default value is type.relation = c("tolerance", "eq.1"). See BC.IND.relation.FRST.

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

Details

The method proposed by (Zhao, 2010) consists of three steps as follows. First, it builds a general lower approximation that is able to deal with misclassification and perturbation. In this case, the fuzzy variable precision rough sets (FVPRS) is used to calculate the lower approximation (see BC.LU.approximation.FRST). Secondly, a discernibility matrix considering a consistence degree is constructed for obtaining rules. The details about the matrix can be seen in BC.discernibility.mat.FRST. Then, we calculate attribute value reduction of every object and perform near-minimal rule set. The final step is to construct rules considering the consistence degree of associated objects.

It should be noted that this function only allows classification 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 following components:

Author(s)

Lala Septem Riza

References

S. Y. Zhao, E. C. C. Tsang, D. G. Chen, and X. Z. Wang, "Building a Rule-based Classifier – A Fuzzy-rough Set Approach", IEEE Trans. on Knowledge and Data Engineering, vol. 22, no. 5, p. 624 - 638 (2010).

See Also

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

Examples

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###########################################################
## Example
##############################################################
data(RoughSetData)
decision.table <- RoughSetData$pima7.dt

control <- list(alpha.precision = 0.01, type.aggregation = c("t.tnorm", "lukasiewicz"),
                type.relation = c("tolerance", "eq.3"), t.implicator = "lukasiewicz")
rules <- RI.GFRS.FRST(decision.table, control)

RoughSets documentation built on May 29, 2017, 7:06 p.m.

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