FSinR (version 2.0.5)

searchAlgorithm: Search algorithm generator

Description

Generates a search function. This function in combination with the evaluator guides the feature selection process. Specifically, the result of calling this function is another function that is passed on as a parameter to the featureSelection function. However, you can run this function directly to perform a search process in the features space.

Usage

searchAlgorithm(searcher, params = list())

Arguments

searcher

Name of the search algorithm. The available search algorithms are:

antColony

Ant colony optimization (ACO). See antColony

breadthFirst

Breadth first search. See breadthFirst

deepFirst

Deep first search. See deepFirst

geneticAlgorithm

Genetic algorithm (GA). See geneticAlgorithm

hillClimbing

Hill-Climbing (HC). See hillClimbing

LasVegas

Las Vegas (LV). See LasVegas

sequentialBackwardSelection

Sequential backward selection (sbs). See sequentialBackwardSelection

sequentialFloatingForwardSelection

Sequential floating forward selection (sffs). See sequentialFloatingForwardSelection

sequentialFloatingBackwardSelection

Sequential floating backward selection (sfbs). See sequentialFloatingBackwardSelection

sequentialForwardSelection

Sequential forward selection (sfs). See sequentialForwardSelection

simulatedAnnealing

Simulated annealing (SA). See simulatedAnnealing

tabu

Tabu search (TS). See tabu

whaleOptimization

Whale optimization algorithm (WOA). See whaleOptimization

params

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

Value

Returns a search function that is used to guide the feature selection process

References

Examples

# NOT RUN {
## Examples of a search algorithm generation

search_method_1 <- searchAlgorithm('antColony')
search_method_2 <- searchAlgorithm('sequentialBackwardSelection')
search_method_3 <- searchAlgorithm('tabu')


## Examples of a search algorithm generation (with parameters)

search_method_1 <- searchAlgorithm('antColony', list(population=25, iter=50, verbose=TRUE))
search_method_2 <- searchAlgorithm('sequentialBackwardSelection', list(stop=TRUE))
search_method_3 <- searchAlgorithm('tabu', list(intensification=1, iterIntensification=25))


## The direct application of this function is an advanced use that consists of using this 
# function directly to perform a search process on a feature space
## Classification problem

# Generates the filter evaluation function
filter_evaluator <- filterEvaluator('determinationCoefficient')

# Generates the search function
search_method <- searchAlgorithm('hillClimbing')
# Performs the search process directly (parameters: dataset, target variable and evaluator)
search_method(iris, 'Species', filter_evaluator)
# }