arulesCBA (version 1.2.0)

wCBA: Classification Based on Association Rules

Description

Build a classifier using a naive rule-weighting algorithm. The algorithm is currently in development, and is not yet formally documented.

Usage

wCBA(formula, data, parameter = NULL, control = NULL,
    sort.parameter = NULL, lhs.support = FALSE, class.weights = NULL,
    disc.method = "mdlp", verbose = FALSE, ...)

Arguments

formula

A symbolic description of the model to be fitted. Has to be of form class ~ .. The class is the variable name (part of the item label before =).

data

A data.frame containing the training data.

parameter, control

Optional parameter and control lists for apriori.

sort.parameter

Ordered vector of arules interest measures (as characters) which are used to sort rules in preprocessing.

lhs.support

Logical variable, which, when set to default value of True, indicates that LHS support should be used for rule mining.

class.weights

Weights that should be assigned to the rows of each class (ordered by appearance in levels(classColumn))

disc.method

Discretization method for factorizing numeric input (default: "mdlp"). See discretizeDF.supervised for more supervised discretization methods.

verbose

Optional logical flag to allow verbose execution, where additional intermediary execution information is printed at runtime.

...

Additional parameters are added to the apriori parameters (e.g., support and confidence).

Value

Returns an object of class CBA representing the trained classifier with fields:

rules

the classifier rule base.

default

default class label.

levels

levels of the class variable.

Details

Mines association rules on input data and creates a weighted-vote classifier where a rules weight is the product of its support and confidence. Default class is set to the most common class in the training data.

See Also

predict.CBA, CBA.object, apriori,

Examples

# NOT RUN {
data("iris")

classifier <- wCBA(Species ~ ., data = iris, supp = 0.05, conf = 0.9)
classifier

predict(classifier, head(iris))
# }