arulesCBA: CBA.object – R documentation

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CBA.object

Objects for Classifiers Based on Association Rules

Description

Objects for classifiers based on association rules have class "CBA". A creator function CBA_ruleset() and several methods are provided.

Usage

CBA_ruleset(formula, rules, method = "first", weights = NULL, default = NULL,
  description = "Custom rule set")

## S3 method for class 'CBA'
print(x, ...)
## S3 method for class 'CBA'
rules(x)
## S3 method for class 'CBA'
predict(object, newdata, type = c("class", "score"), ...)

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 `=`).
`rules`	A set of class association rules mined with `mineCars` or `apriori` (from arules).
`method`	Classification method `"first"` found rule or `"majority"`.
`weights`	Rule weights for method majority. Either a quality measure available in `rules` or a numeric vector of the same length are `rules` can be specified. If missing, then equal weights are used
`default`	Default class of the form `variable=level`. If not specified then the most frequent RHS in rules is used.

`description`	Description field used when the classifier is printed.
`x, object`	An object of class `CBA`.
`newdata`	A data.frame or transactions containing rows of new entries to be classified.
`type`	Predict `"class"` labels. Some classifiers can also return `"scores"`.
`...`	Additional arguments currently not used.

Details

CBA_ruleset creates a new object of class CBA using the provides rules as the rule base. For method "first", the user needs to make sure that the rules are predictive and sorted from most to least predictive.

Value

CBA_ruleset() returns an object of class CBA representing the trained classifier with fields:

`formula`	used formula.
`discretization`	discretization information.
`rules`	the classifier rule base.
`default`	default class label ot `NA`.
`weights`	rule weights.
`biases`	class biases.
`method`	classification method.
`description`	description in human readable form.

predict returns predicted labels for newdata.

rules returns the rule base.

Author(s)

Michael Hahsler

Examples

data("iris")

# discretize and create transactions
iris.disc <- discretizeDF.supervised(Species ~., iris)
trans <- as(iris.disc, "transactions")

# create rule base with CARs
cars <- mineCARs(Species ~ ., trans, parameter = list(support = .01, confidence = .8))

cars <- cars[!is.redundant(cars)]
cars <- sort(cars, by = "conf")

# create classifier
cl <- CBA_ruleset(Species ~ ., cars)
cl

# look at the rule base
rules(cl)

# make predictions
prediction <- predict(cl, trans)
table(prediction, response(Species ~ ., trans))

# use weighted majority
cl <- CBA_ruleset(Species ~ ., cars, method = "majority", weights = "lift")
cl

prediction <- predict(cl, trans)
table(prediction, response(Species ~ ., trans))

arulesCBA

Classification Based on Association Rules

v1.2.0

GPL-3

Authors

Michael Hahsler [aut, cre, cph], Ian Johnson [aut, cph], Tyler Giallanza [ctb]

Initial release

2020-4-17