RI.LEM2Rules.RST: Rule induction using the LEM2 algorithm

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

Description

An implementation of LEM2 (Learning from Examples Module, version 2) for induction of decision rules, originally proposed by J.W. Grzymala-Busse.

Usage

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RI.LEM2Rules.RST(decision.table)

Arguments

decision.table

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

Value

An object of a class "RuleSetRST". For details see RI.indiscernibilityBasedRules.RST.

Author(s)

Andrzej Janusz

References

J.W. Grzymala-Busse, "A New Version of the Rule Induction System LERS", Fundamenta Informaticae, 31, p. 27 - 39 (1997).

See Also

predict.RuleSetFRST, RI.indiscernibilityBasedRules.RST, RI.CN2Rules.RST, RI.AQRules.RST.

Examples

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###########################################################
## Example
##############################################################
data(RoughSetData)
wine.data <- RoughSetData$wine.dt
set.seed(13)
wine.data <- wine.data[sample(nrow(wine.data)),]

## Split the data into a training set and a test set,
## 60% for training and 40% for testing:
idx <- round(0.6 * nrow(wine.data))
wine.tra <-SF.asDecisionTable(wine.data[1:idx,],
                              decision.attr = 14,
                              indx.nominal = 14)
wine.tst <- SF.asDecisionTable(wine.data[(idx+1):nrow(wine.data), -ncol(wine.data)])

true.classes <- wine.data[(idx+1):nrow(wine.data), ncol(wine.data)]

## discretization:
cut.values <- D.discretization.RST(wine.tra,
                                   type.method = "local.discernibility",
                                   maxNOfCuts = 1)
data.tra <- SF.applyDecTable(wine.tra, cut.values)
data.tst <- SF.applyDecTable(wine.tst, cut.values)

## rule induction from the training set:
rules <- RI.LEM2Rules.RST(data.tra)
rules

## predicitons for the test set:
pred.vals <- predict(rules, data.tst)

## checking the accuracy of predictions:
mean(pred.vals == true.classes)


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