RoughSets (version 1.3-7)

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

Description

Rule induction from indiscernibility classes.

Usage

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

Value

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

• uniqueCls: a vector of possible decision classes,

• supportDenominator: an integer giving the number of objects in the data,

• clsProbs: a vector giving the a priori probability of the decision classes,

• majorityCls: a class label representing the majority class in the data,

• method: the type a rule induction method used for computations,

• dec.attr: a name of the decision attribute in the data,

• colnames: a vector of conditional attribute names.

Each rule is a list with the following elements:

• idx: a vector of indexes of attribute that are used in antecedent of a rule,

• values: a vector of values of attributes indicated by idx,

• consequent: a value of the consequent of a rule,

• support: a vactor of integers indicating objects from the data, which support a given rule,

• laplace: ia numeric value representing the Laplace estimate of the rule's confidence.

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.

Author

Andrzej Janusz

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.

See Also

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

Examples

###########################################################
## 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

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