filterEvaluator: Filter measure generator

In FSinR: Feature Selection

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.

Author(s)

Francisco Aragón Royón

References

\insertAllCited

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'))

## End(Not run)

FSinR documentation built on Nov. 23, 2020, 5:10 p.m.