FCRA_C {RKEEL}

FCRA_C KEEL Classification Algorithm

Description

FCRA_C Classification Algorithm from KEEL.

Usage

FCRA_C(train, test, generations, pop_size, length_S_C, WCAR,
   WV, crossover_prob, mut_prob, n1, n2, max_iter,
   linguistic_values, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

generations

generations. Default value = 50

pop_size

pop_size. Default value = 30

length_S_C

length_S_C. Default value = 10

WCAR

WCAR. Default value = 10.0

WV

WV. Default value = 1.0

crossover_prob

crossover_prob. Default value = 1.0

mut_prob

mut_prob. Default value = 0.01

n1

n1. Default value = 0.001

n2

n2. Default value = 0.1

max_iter

max_iter. Default value = 100

linguistic_values

linguistic_values. Default value = 5

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::FCRA_C(data_train, data_test, generations=10, pop_size=10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions