FSinR (version 2.0.5)

hybridFeatureSelection: Hybrid Feature Selection Proccess

Description

Performs the hybrid feature selection process. Given a hybrid search algorithm and an two evaluation methods, it uses the hybrid search algorithm in combination with the evaluation results to guide the feature selection process to an optimal subset.

Usage

hybridFeatureSelection(data, class, hybridSearcher, evaluator_1, evaluator_2)

Arguments

data

A data.frame with the input dataset where the examples are in the rows and the features and the target variable are in the columns. The dataset should be discrete (feature columns are expected to be factors) if the following filter methods are used as evaluation methods: Rough Set Consistency, Binary Consistency, IE Consistency, IEP Consistency, Mutual Information, Gain Ratio, Symmetrical Uncertain, Gini Index or MDLC. The Jd and F-Score filter methods only work on classification problems with 2 classes in the target variable.

class

The name of the dependent variable

hybridSearcher

The algorithm to guide the hybrid search in the feature space. See hybridSearchAlgorithm.

evaluator_1

The first evaluation method. This method can be a filter (see filterEvaluator) or a wrapper method (see wrapperEvaluator).

evaluator_2

The second evaluation method. This method can be a filter (see filterEvaluator) or a wrapper method (see wrapperEvaluator). If the LCC algorithm is used, the measure must evaluate feature sets.

Value

A list is returned with the results of the hybrid feature selection process:

bestFeatures

A vector with all features. Selected features are marked with 1, unselected features are marked with 0.

bestValue

Evaluation measure obtained with the feature selection.

evaluationType_1

Type of evaluation based on how the features have been evaluated.

evaluationMethod_1

Evaluation method used for the first evaluator.

measureType_1

Type of evaluation measure for the first evaluator.

evaluationType_2

Type of evaluation based on how the features have been evaluated for the first evaluator.

evaluationMethod_2

Evaluation method used for the second evaluator.

measureType_2

Type of evaluation measure for the second evaluator.

searchMethod

Search method used during the feature selection process for the second evaluator.

target

A character indicating if the objective of the process is to minimize or maximize the evaluation measure.

numFeatures

Number of features in the problem.

xNames

Name of the features.

yNames

Name of the dependent variable.

time

Value of class 'proc_time' containing the user time, system time, and total time of the feature selection process.

References

Examples

# NOT RUN {
## Examples of the hybrid feature selection process
## Classification problem with filter

# Generates the first filter evaluation function (individual or set measure)
f_evaluator_1 <- filterEvaluator('determinationCoefficient')
# Generates the second filter evaluation function (mandatory set measure)
f_evaluator_2 <- filterEvaluator('ReliefFeatureSetMeasure')
# Generates the hybrid search function
hybrid_search_method <- hybridSearchAlgorithm('LCC')
# Runs the hybrid feature selection process
res <- hybridFeatureSelection(iris,'Species',hybrid_search_method,f_evaluator_1,f_evaluator_2)


## Classification problem with wrapper

# Generates the first wrapper evaluation function (individual or set measure)
w_evaluator_1 <- wrapperEvaluator('rf')
# Generates the second wrapper evaluation function (mandatory set measure)
w_evaluator_2 <- wrapperEvaluator('knn')
# Generates the hybrid search function
hybrid_search_method <- hybridSearchAlgorithm('LCC')
# Runs the hybrid feature selection process
res <- hybridFeatureSelection(iris,'Species',hybrid_search_method,w_evaluator_1,w_evaluator_2)


## Classification problem mixed (with filter & wrapper)

# Generates the first filter evaluation function (individual or set measure)
f_evaluator <- filterEvaluator('determinationCoefficient')
# Generates the second wrapper evaluation function (mandatory set measure)
w_evaluator <- wrapperEvaluator('knn')
# Generates the hybrid search function
hybrid_search_method <- hybridSearchAlgorithm('LCC')
# Runs the hybrid feature selection process
res <- hybridFeatureSelection(iris, 'Species', hybrid_search_method, f_evaluator, w_evaluator)
# }
# NOT RUN {
# }