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

Run this code

# 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)
# }