PSO_ACO_C KEEL Classification Algorithm

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Description

PSO_ACO_C Classification Algorithm from KEEL.

Usage

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PSO_ACO_C(train, test, max_uncovered_samples, min_saples_by_rule,
   max_iterations_without_converge, enviromentSize, numParticles,
   x, c1, c2, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

max_uncovered_samples

max_uncovered_samples. Default value = 20

min_saples_by_rule

min_saples_by_rule. Default value = 2

max_iterations_without_converge

max_iterations_without_converge. Default value = 100

enviromentSize

enviromentSize. Default value = 3

numParticles

numParticles. Default value = 100

x

x. Default value = 0.72984

c1

c1. Default value = 2.05

c2

c2. Default value = 2.05

seed

Seed for random numbers. If it is not assigned a value, the seed will be a random number

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

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data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::PSO_ACO_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

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