Tan_GP_C KEEL Classification Algorithm

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Description

Tan_GP_C Classification Algorithm from KEEL.

Usage

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Tan_GP_C(train, test, population_size, max_generations,
   max_deriv_size, rec_prob, mut_prob, copy_prob, w1, w2,
   elitist_prob, support, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

population_size

population_size. Default value = 150

max_generations

max_generations. Default value = 100

max_deriv_size

max_deriv_size. Default value = 20

rec_prob

rec_prob. Default value = 0.8

mut_prob

mut_prob. Default value = 0.1

copy_prob

copy_prob. Default value = 0.01

w1

w1. Default value = 0.7

w2

w2. Default value = 0.8

elitist_prob

elitist_prob. Default value = 0.06

support

support. Default value = 0.03

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::Tan_GP_C(data_train, data_test)
algorithm <- RKEEL::Tan_GP_C(data_train, data_test, population_size = 5, max_generations = 10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

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