CHC-C: CHC_C KEEL Classification Algorithm

In RKEEL: Using 'KEEL' in R Code

CHC_C KEEL Classification Algorithm

Description

CHC_C Classification Algorithm from KEEL.

Usage

CHC_C(train, test, pop_size, evaluations, alfa, restart_change,
   prob_restart, prob_diverge, k, distance, seed)

Arguments

train	Train dataset as a data.frame object
test	Test dataset as a data.frame object
pop_size	pop_size. Default value = 50
evaluations	evaluations. Default value = 10000
alfa	alfa. Default value = 0.5
restart_change	restart_change. Default value = 0.35
prob_restart	prob_restart. Default value = 0.25
prob_diverge	prob_diverge. Default value = 0.05
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::CHC_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

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