NNS.boost: NNS Boost

In NNS: Nonlinear Nonparametric Statistics

NNS Boost

Description

Ensemble method for classification using the predictions of the NNS multivariate regression NNS.reg collected from uncorrelated feature combinations.

Usage

NNS.boost(
  IVs.train,
  DV.train,
  IVs.test = NULL,
  type = NULL,
  depth = NULL,
  learner.trials = 100,
  epochs = NULL,
  CV.size = NULL,
  balance = FALSE,
  ts.test = NULL,
  folds = 5,
  threshold = NULL,
  obj.fn = expression(sum((predicted - actual)^2)),
  objective = "min",
  extreme = FALSE,
  features.only = FALSE,
  feature.importance = TRUE,
  status = TRUE
)

Arguments

IVs.train	a matrix or data frame of variables of numeric or factor data types.
DV.train	a numeric or factor vector with compatible dimensions to (IVs.train).
IVs.test	a matrix or data frame of variables of numeric or factor data types with compatible dimensions to (IVs.train). If NULL, will use (IVs.train) as default.
type	NULL (default). To perform a classification of discrete integer classes from factor target variable (DV.train) with a base category of 1, set to (type = "CLASS"), else for continuous (DV.train) set to (type = NULL).
depth	options: (integer, NULL, "max"); (depth = NULL)(default) Specifies the order parameter in the NNS.reg routine, assigning a number of splits in the regressors, analogous to tree depth.
learner.trials	integer; 100 (default) Sets the number of trials to obtain an accuracy threshold level. If the number of all possible feature combinations is less than selected value, the minimum of the two values will be used.
epochs	integer; 2*length(DV.train) (default) Total number of feature combinations to run.
CV.size	numeric [0, 1]; NULL (default) Sets the cross-validation size. Defaults to a random value between 0.2 and 0.33 for a random sampling of the training set.
balance	logical; FALSE (default) Uses both up and down sampling from caret to balance the classes. type="CLASS" required.
ts.test	integer; NULL (default) Sets the length of the test set for time-series data; typically 2*h parameter value from NNS.ARMA or double known periods to forecast.
folds	integer; 5 (default) Sets the number of folds in the NNS.stack procedure for optimal n.best parameter.
threshold	numeric; NULL (default) Sets the obj.fn threshold to keep feature combinations.
obj.fn	expression; expression( sum((predicted - actual)^2) ) (default) Sum of squared errors is the default objective function. Any expression() using the specific terms predicted and actual can be used. Automatically selects an accuracy measure when (type = "CLASS").
objective	options: ("min", "max") "max" (default) Select whether to minimize or maximize the objective function obj.fn.
extreme	logical; FALSE (default) Uses the maximum (minimum) threshold obtained from the learner.trials, rather than the upper (lower) quintile level for maximization (minimization) objective.
features.only	logical; FALSE (default) Returns only the final feature loadings along with the final feature frequencies.
feature.importance	logical; TRUE (default) Plots the frequency of features used in the final estimate.
status	logical; TRUE (default) Prints status update message in console.

Value

Returns a vector of fitted values for the dependent variable test set $results, and the final feature loadings $feature.weights, along with final feature frequencies $feature.frequency.

Note

Like a logistic regression, the (type = "CLASS") setting is not necessary for target variable of two classes e.g. [0, 1]. The response variable base category should be 1 for classification problems.

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. (2016) "Classification Using NNS Clustering Analysis" https://www.ssrn.com/abstract=2864711

Examples

 ## Using 'iris' dataset where test set [IVs.test] is 'iris' rows 141:150.
 ## Not run: 
 a <- NNS.boost(iris[1:140, 1:4], iris[1:140, 5],
 IVs.test = iris[141:150, 1:4],
 epochs = 100, learner.trials = 100,
 type = "CLASS", depth = NULL)

 ## Test accuracy
 mean(a$results == as.numeric(iris[141:150, 5]))
 
## End(Not run)

NNS documentation built on Jan. 8, 2023, 1:08 a.m.