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 RuleSetRST
predict(object, newdata, ...)

Value

A data.frame with a single column containing predictions for objects from 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 RI.indiscernibilityBasedRules.RST, RI.CN2Rules.RST, RI.LEM2Rules.RST or RI.AQRules.RST.

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 correspond to columns of a data set used for the rule induction.

...

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 RI.AQRules.RST which accept a named parameter votingMethod. This parameter can be used to pass a custom function for computing a weight of a voting rule. There are three such functions already available in the package:

X.ruleStrength is the default voting method. It is defined as a product of a cardinality of a support of a rule and the length of this rule. See X.ruleStrength.
X.laplace corresponds to a voting weighted by the Laplace estimates of rules' confidence. See X.laplace.
X.rulesCounting corresponds to voting by counting the matching rules for different decision classes. See X.rulesCounting.

A custom function passed using the votingMethod can get additional parameters using the ... interface.

Author

Andrzej Janusz

Examples

Run this code

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