dceGMDH {GMDH2}

Diverse Classifiers Ensemble Based on GMDH-Type Neural Network Algorithm for Binary Classification

Description

dceGMDH makes a binary classification via diverse classifiers ensemble Based on GMDH-Type Neural Network (dce-GMDH) Algorithm.

Usage

dceGMDH(x.train, y.train, x.valid, y.valid, alpha = 0.6, maxlayers = 10, 
  maxneurons = 15, exCriterion = "MSE", verbose = TRUE, svm_options, 
   randomForest_options, naiveBayes_options, cv.glmnet_options, nnet_options, ...)

Arguments

x.train

a n1xp matrix to be included in model construction, n1 is the number of observations and p is the number of variables.

y.train

a factor of binary response variable to be included in model construction.

x.valid

a n2xp matrix to be used for neuron selection, n2 is the number of observations and p is the number of variables.

y.valid

a factor of binary response variable to be used for neuron selection.

alpha

the selection pressure in a layer. Defaults alpha = 0.6.

maxlayers

the number of maximum layers. Defaults maxlayers = 10.

maxneurons

the number of maximum neurons selected in each layer. Defaults maxneurons = 15.

exCriterion

a character string to select an external criteria. "MSE": Mean Square Error, "MAE": Mean Absolute Error. Default is set to "MSE".

verbose

a logical for printing summary output to R console.

svm_options

a list for options of svm.

randomForest_options

a list for options of randomForest.

naiveBayes_options

a list for options of naiveBayes.

cv.glmnet_options

a list for options of cv.glmnet.

nnet_options

a list for options of nnet.

...

not used currently.

Value

A list with class "dceGMDH" and "GMDHplot" containing the following components:

architecture

all objects stored in construction process of network

nlayer

the number of layers

neurons

the number of neurons in layers

sneurons

the number of selected neurons in layers

structure

the summary structure of the process

levels

the levels of binary response

base_perf

the performances of the classifiers on validation set at base training

base_models

the constructed base classifiers models

classifiers

the names of assembled classifiers

plot_list

the list of objects to be used in plot.GMDHplot

Author(s)

Osman Dag, Erdem Karabulut, Reha Alpar

References

Dag, O., Karabulut, E., Alpar, R. (2019). GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms - R Package and Web-Based Tool. International Journal of Computational Intelligence Systems, 12:2, 649-660.

Dag, O., Kasikci, M., Karabulut, E., Alpar, R. (2022). Diverse Classifiers Ensemble Based on GMDH-Type Neural Network Algorithm for Binary Classification. Communications in Statistics - Simulation and Computation, 51:5, 2440-2456.

Examples

library(GMDH2)

library(mlbench)
data(BreastCancer)

data <- BreastCancer

# to obtain complete observations
completeObs <- complete.cases(data)
data <- data[completeObs,]

x <- data.matrix(data[,2:10])
y <- data[,11]

seed <- 12345
set.seed(seed)
nobs <- length(y)

# to split train, validation and test sets

indices <- sample(1:nobs)

ntrain <- round(nobs*0.6,0)
nvalid <- round(nobs*0.2,0)
ntest <- nobs-(ntrain+nvalid)

train.indices <- sort(indices[1:ntrain])
valid.indices <- sort(indices[(ntrain+1):(ntrain+nvalid)])
test.indices <- sort(indices[(ntrain+nvalid+1):nobs])


x.train <- x[train.indices,]
y.train <- y[train.indices]

x.valid <- x[valid.indices,]
y.valid <- y[valid.indices]

x.test <- x[test.indices,]
y.test <- y[test.indices]

set.seed(seed)
# to construct model via dce-GMDH algorithm
model <- dceGMDH(x.train, y.train, x.valid, y.valid)

# to obtain predicted classes for test set
predict(model, x.test)