RI.CN2Rules.RST: Rule induction using a version of CN2 algorithm

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

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)

RoughSets documentation built on Dec. 16, 2019, 1:37 a.m.