RoughSets (version 1.2-1)

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

Description

Rule induction from indiscernibility classes.

Usage

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.

Value

• An object of a class "RuleSetRST", which is a list with additional attributes:
  • uniqueCls: a vector of possible decision classes,
  • 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 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 byidx,
  • 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.

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