RI.indiscernibilityBasedRules.RST: Rule induction from indiscernibility classes.

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

View source: R/RuleInduction.R

Description

Rule induction from indiscernibility classes.

Usage

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RI.indiscernibilityBasedRules.RST(decision.table, feature.set)

Arguments

decision.table

an object inheriting from the "DecisionTable" class, which represents a decision system. See SF.asDecisionTable.

feature.set

an object inheriting from the "FeatureSubset" class which is a typical output of feature selection methods based on RST e.g. FS.greedy.heuristic.reduct.RST. See also FS.reduct.computation, FS.feature.subset.computation and FS.all.reducts.computation based on RST.

Details

This function generates "if-then" decision rules from indiscernibility classes defined by a given subset of conditional attributes.

After obtaining the rules, decision classes of new objects can be predicted using the predict method or by a direct call to predict.RuleSetRST.

Value

An object of a class "RuleSetRST", which is a list with additional attributes:

Each rule is a list with the following elements:

Author(s)

Andrzej Janusz

See Also

predict.RuleSetRST, RI.CN2Rules.RST, RI.LEM2Rules.RST, RI.AQRules.RST.

Examples

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###########################################################
## Example
##############################################################
data(RoughSetData)
hiring.data <- RoughSetData$hiring.dt

## determine feature subset/reduct
reduct <- FS.reduct.computation(hiring.data,
                                method = "permutation.heuristic",
                                permutation = FALSE)

rules <- RI.indiscernibilityBasedRules.RST(hiring.data, reduct)
rules

janusza/RoughSets documentation built on May 31, 2018, 11:11 a.m.