dceGMDH | R Documentation |
dceGMDH
makes a binary classification via diverse classifiers ensemble Based on GMDH-Type Neural Network (dce-GMDH) Algorithm.
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, ...)
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 |
randomForest_options |
a list for options of |
naiveBayes_options |
a list for options of |
cv.glmnet_options |
a list for options of |
nnet_options |
a list for options of |
... |
not used currently. |
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 |
Osman Dag, Erdem Karabulut, Reha Alpar
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.
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)
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