RI.CN2Rules.RST {RoughSets}

Rule induction using a version of CN2 algorithm

Description

An implementation of verions of the famous CN2 algorithm for induction of decision rules, proposed by P.E. Clark and T. Niblett.

Usage

RI.CN2Rules.RST(decision.table, K = 3)

Arguments

decision.table

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

K

a positive integer that controls a complexity of the algorithm. In each iteration K best rule predicates are extended by all possible descriptors.

Value

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

Author(s)

Andrzej Janusz

References

P.E. Clark and T. Niblett, "The CN2 Induction algorithm", Machine Learning, 3, p. 261 - 284 (1986).

See Also

predict.RuleSetFRST, RI.indiscernibilityBasedRules.RST, RI.LEM2Rules.RST, RI.AQRules.RST.

Examples

###########################################################
## 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 = "unsupervised.quantiles",
                                   nOfIntervals = 3)
data.tra <- SF.applyDecTable(wine.tra, cut.values)
data.tst <- SF.applyDecTable(wine.tst, cut.values)

## rule induction from the training set:
rules <- RI.CN2Rules.RST(data.tra, K = 5)
rules

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

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