kernelFactory {kernelFactory} | R Documentation |
Binary classification with Kernel Factory
Description
kernelFactory
implements an ensemble method for kernel machines (Ballings and Van den Poel, 2013).
Usage
kernelFactory(x = NULL, y = NULL, cp = 1, rp = round(log(nrow(x), 10)),
method = "burn", ntree = 500, filter = 0.01, popSize = rp * cp * 7,
iters = 80, mutationChance = 1/(rp * cp), elitism = max(1, round((rp *
cp) * 0.05)), oversample = TRUE)
Arguments
x |
A data frame of predictors (numeric, integer or factor). Categorical variables need to be factors. Indicator values should not be too imbalanced because this might produce constants in the subsetting process. |
y |
A factor containing the response vector. Only {0,1} is allowed. |
cp |
The number of column partitions. |
rp |
The number of row partitions. |
method |
Can be one of the following: POLynomial kernel function ( |
ntree |
Number of trees in the Random Forest base classifiers. |
filter |
either NULL (deactivate) or a percentage denoting the minimum class size of dummy predictors. This parameter is used to remove near constants. For example if nrow(xTRAIN)=100, and filter=0.01 then all dummy predictors with any class size equal to 1 will be removed. Set this higher (e.g., 0.05 or 0.10) in case of errors. |
popSize |
Population size of the genetic algorithm. |
iters |
Number of generations of the genetic algorithm. |
mutationChance |
Mutationchance of the genetic algorithm. |
elitism |
Elitism parameter of the genetic algorithm. |
oversample |
Oversample the smallest class. This helps avoid problems related to the subsetting procedure (e.g., if rp is too high). |
Value
An object of class kernelFactory
, which is a list with the following elements:
trn |
Training data set. |
trnlst |
List of training partitions. |
rbfstre |
List of used kernel functions. |
rbfmtrX |
List of augmented kernel matrices. |
rsltsKF |
List of models. |
cpr |
Number of column partitions. |
rpr |
Number of row partitions. |
cntr |
Number of partitions. |
wghts |
Weights of the ensemble members. |
nmDtrn |
Vector indicating the numeric (and integer) features. |
rngs |
Ranges of numeric predictors. |
constants |
To exclude from newdata. |
Author(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
References
Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913.
See Also
Examples
#Credit Approval data available at UCI Machine Learning Repository
data(Credit)
#take subset (for the purpose of a quick example) and train and test
Credit <- Credit[1:100,]
train.ind <- sample(nrow(Credit),round(0.5*nrow(Credit)))
#Train Kernel Factory on training data
kFmodel <- kernelFactory(x=Credit[train.ind,names(Credit)!= "Response"],
y=Credit[train.ind,"Response"], method=random)
#Deploy Kernel Factory to predict response for test data
#predictedresponse <- predict(kFmodel, newdata=Credit[-train.ind,names(Credit)!= "Response"])