A version of the AQ algorithm which was originally proposed by R.S. Michalski. This implamentation is based on a concept of a local (objectrelative) decision reduct from RST.
1  RI.AQRules.RST(decision.table, confidence = 1, timesCovered = 1)

decision.table 
an object inheriting from the 
confidence 
a numeric value giving the minimal confidence of computed rules. 
timesCovered 
a positive integer. The algorithm will try to find a coverage of training examples with rules,
such that each example is covered by at least 
An object of a class "RuleSetRST"
. For details see RI.indiscernibilityBasedRules.RST
.
Andrzej Janusz
R.S. Michalski, K. Kaufman, J. Wnek: "The AQ Family of Learning Programs: A Review of Recent Developments and an Exemplary Application", Reports of Machine Learning and Inference Laboratory, George Mason University (1991)
predict.RuleSetFRST
, RI.indiscernibilityBasedRules.RST
, RI.CN2Rules.RST
,
RI.LEM2Rules.RST
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  ###########################################################
## 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.AQRules.RST(data.tra, confidence = 0.9, timesCovered = 3)
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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