copulaClassifier {MLCOPULA}

Train a classification model using copula functions.

Description

It trains a classification model based on copulas, the joint density of copulas is built with tree or chain graphic model, as shown in Salinas-Gutiérrez et al., 2014.

Usage

copulaClassifier(
  X,
  y,
  distribution = "kernel",
  copula = "frank",
  weights = "likelihood",
  graph_model = "tree",
  k = 7,
  m = 7,
  method_grid = "ml"
)

Arguments

X

Data frame with n samples and d>1 predictor variables.

y

a vector of size n, with the classes to predict.

distribution

Marginal distribution to be used: "normal" or "kernel", by default kernel.

copula

Character or vector with the name of the copula to be used: "frank", "gaussian", "clayton", "joe", "gumbel", "amh","grid", by default "frank". For parametric copulas, "frank", "gaussian", "clayton", "joe", "gumbel", "amh", one or more copulas can be selected. For nonparametric copula, only "grid" can be selected. See the examples for more details.

weights

Character with the weight construction method: "likelihood" or "mutual_information", by default "likelihood".

graph_model

Character with the graphical model structure: "tree" or "chain", by default "tree".

k

Only for grid copula. Positive integer indicating the number of subintervals for the U2 variable.

m

Only for grid copula. Positive integer indicating the number of subintervals for the U1 variable.

method_grid

Only for grid copula. Fitting method, least squares "ls" or maximum likelihood "ml", by default "ml".

Value

Returns a list with the trained model.

References

Salinas-Gutiérrez, R., Hernández-Aguirre, A., Villa-Diharce, E.R. (2014). Copula selection for graphical models in continuous Estimation of Distribution Algorithms. Computational Statistics, 29(3–4):685–713. doi:10.1007/s00180-013-0457-y

Examples

X <- iris[,1:4]
y <- iris$Species
model <- copulaClassifier(X = X, y = y, copula = "frank",
                      distribution = "kernel", graph_model = "tree")
y_pred <- copulaPredict(X = X, model = model)
table(y,y_pred$class)
#Example 2
X <- iris[,1:4]
y <- iris$Species
model <- copulaClassifier(X = X, y = y, copula = c("frank","clayton"), 
                        distribution = "kernel", graph_model = "chain")
y_pred <- copulaPredict(X = X, model = model)
table(y,y_pred$class)