FCRA-C: FCRA_C KEEL Classification Algorithm

In RKEEL: Using 'KEEL' in R Code

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

RKEEL documentation built on Sept. 15, 2023, 1:08 a.m.