RoughSets (version 1.2-1)

predict.RuleSetRST: Prediction of decision classes using rule-based classifiers.

Description

The prediction method for objects inheriting from the RuleSetRST class.

Usage

## S3 method for class 'RuleSetRST':
predict(object, newdata, ...)

Arguments

object

an object inheriting from the "RuleSetRST" class. Such objects are typically produced by implementations of rule induction methods, which derives from the rough set theory (RST), such as

newdata

an object inheriting from the "DecisionTable" class, which represents the data for which predictions are to be made. See SF.asDecisionTable. Columns in newdata should co

...

additional parameters for a rule voting strategy. It can be applied only to the methods which classify new objects by voting. Currently, those methods include RI.LEM2Rules.RST and

Value

• A data.frame with a single column containing predictions for objects from newdata.

See Also

Rule induction methods implemented within RST include: RI.indiscernibilityBasedRules.RST, RI.CN2Rules.RST, RI.LEM2Rules.RST and RI.AQRules.RST. For details on rule induction methods based on FRST see RI.GFRS.FRST and RI.hybridFS.FRST.

Examples

##############################################
## Example: Classification Task
##############################################
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.LEM2Rules.RST(data.tra)

## predicitons for the test set:
pred.vals1 <- predict(rules, data.tst)
pred.vals2 <- predict(rules, data.tst,
                      votingMethod = X.laplace)
pred.vals3 <- predict(rules, data.tst,
                      votingMethod = X.rulesCounting)

## checking the accuracy of predictions:
mean(pred.vals1 == true.classes)
mean(pred.vals2 == true.classes)
mean(pred.vals3 == true.classes)