RI.AQRules.RST: Rule induction using the AQ algorithm
In janusza/RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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

A version of the AQ algorithm which was originally proposed by R.S. Michalski. This implamentation is based on a concept of a local (object-relative) decision reduct from RST.

1	RI.AQRules.RST(decision.table, confidence = 1, timesCovered = 1)

`decision.table`	an object inheriting from the `"DecisionTable"` class, which represents a decision system. See `SF.asDecisionTable`.
`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 `timesCovered` rules and no rule can be removed from the coverage without breaking this property. This is not always possible - there is a chance that some rules are duplicated if the value of `timesCovered` is larger than 1.

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.

###########################################################
## 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)