RWNN (version 0.4)

ed_rwnn: Ensemble deep random weight neural networks

Description

Use multiple layers to create deep ensemble random weight neural network models.

Usage

ed_rwnn(
  formula,
  data = NULL,
  n_hidden,
  lambda = 0,
  method = NULL,
  type = NULL,
  control = list()
)
# S3 method for formula
ed_rwnn(
  formula,
  data = NULL,
  n_hidden,
  lambda = 0,
  method = NULL,
  type = NULL,
  control = list()
)

Value

An ERWNN-object.

Arguments

formula

A formula specifying features and targets used to estimate the parameters of the output layer.

data

A data-set (either a data.frame or a tibble) used to estimate the parameters of the output layer.

n_hidden

A vector of integers designating the number of neurons in each of the hidden layers (the length of the list is taken as the number of hidden layers).

lambda

The penalisation constant(s) passed to either rwnn or ae_rwnn (see method argument).

method

The penalisation type passed to ae_rwnn. Set to NULL (default), "l1", or "l2". If NULL, rwnn is used as the base learner.

type

A string indicating whether this is a regression or classification problem.

control

A list of additional arguments passed to the control_rwnn function.

References

Shi Q., Katuwal R., Suganthan P., Tanveer M. (2021) "Random vector functional link neural network based ensemble deep learning." Pattern Recognition, 117, 107978.

Examples

n_hidden <- c(20, 15, 10, 5)
lambda <- 0.01

#
m <- ed_rwnn(y ~ ., data = example_data, n_hidden = n_hidden, lambda = lambda)