SGA_C {RKEEL}

SGA_C KEEL Classification Algorithm

Description

SGA_C Classification Algorithm from KEEL.

Usage

SGA_C(train, test, mut_prob_1to0, mut_prob_0to1, cross_prob,
   pop_size, evaluations, alfa, selection_type, k,
   distance, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

mut_prob_1to0

mut_prob_1to0. Default value = 0.01

mut_prob_0to1

mut_prob_0to1. Default value = 0.001

cross_prob

cross_prob. Default value = 1

pop_size

pop_size. Default value = 50

evaluations

evaluations. Default value = 10000

alfa

alfa. Default value = 0.5

selection_type

selection_type. Default value = "orden_based"

k

k. Default value = 1

distance

distance. Default value = "Euclidean"

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

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

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

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions