RI.indiscernibilityBasedRules.RST: Rule induction from indiscernibility classes.
In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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

Rule induction from indiscernibility classes.

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

`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.

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.

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.

Andrzej Janusz

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

###########################################################
## Example
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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