FSinR (version 2.0.5)

filterEvaluator: Filter measure generator

Description

Generates a filter function to be used as an evaluator in the feature selection proccess. More specifically, the result of calling this function is another function that is passed on as a parameter to the featureSelection function. However, you can also run this function directly to generate an evaluation measure.

Usage

filterEvaluator(filter, params = list())

Arguments

filter

Name of the filter method. The available filter methods are:

binaryConsistency

Binary consistency measure. See binaryConsistency

chiSquared

Chi squared measure. See chiSquared

cramer

Cramer V measure. See cramer

determinationCoefficient

R Squared, to continous features. See determinationCoefficient

fscore

F-score measure. See fscore

gainRatio

The gain ratio measure. See gainRatio

giniIndex

Gini index measure. See giniIndex

IEConsistency

Inconsistent Examples consistency measure. See IEConsistency

IEPConsistency

Inconsistent Examples Pairs consistency measure. See chiSquared

Jd

Jd evaluation measure. See Jd

MDLC

MDLC evaluation measure. See MDLC

mutualInformation

The mutual information measure. See mutualInformation

roughsetConsistency

Rough Set consistency measure. See roughsetConsistency

relief

Relief. See relief

ReliefFeatureSetMeasure

Relief Feature Set Measure evaluation measure. See ReliefFeatureSetMeasure

symmetricalUncertain

Symmetrical uncertain measure. See symmetricalUncertain

params

List with the parameters of each filter method. For more details see each method. Default: empty list.

Value

Returns a filter method that is used to generate an evaluation measure.

References

Examples

# NOT RUN {
## Examples of a filter evaluator generation

filter_evaluator_1 <- filterEvaluator('cramer')
filter_evaluator_2 <- filterEvaluator('gainRatio')
filter_evaluator_3 <- filterEvaluator('MDLC')


## Examples of a filter evaluator generation (with parameters)

filter_evaluator_1 <- filterEvaluator('relief', list(neighbours.count=4, sample.size=15))
filter_evaluator_2 <- filterEvaluator('ReliefFeatureSetMeasure', list(iterations = 10))


## The direct application of this function is an advanced use that consists of using this 
# function directly to evaluate a set of features
## Classification problem

# Generates the filter evaluation function
filter_evaluator <- filterEvaluator('ReliefFeatureSetMeasure')
# Evaluates features directly (parameters: dataset, target variable and features)
filter_evaluator(iris,'Species',c('Sepal.Length','Sepal.Width','Petal.Length','Petal.Width'))
# }