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

In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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. 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:

• 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.

Author(s)

Andrzej Janusz

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

RoughSets documentation built on May 29, 2024, 7:34 a.m.