Type: Package
Title: Kernel Factory: An Ensemble of Kernel Machines
Version: 0.3.0
Date: 2015-09-29
Imports: randomForest, AUC, genalg, kernlab, stats
Author: Michel Ballings, Dirk Van den Poel
Maintainer: Michel Ballings <Michel.Ballings@GMail.com>
Description: Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Packaged: 2015-09-29 11:52:01 UTC; michelballings
Repository: CRAN
Date/Publication: 2015-09-29 17:33:15

Credit approval (Frank and Asuncion, 2010)

Description

Credit contains credit card applications. The dataset has a good mix of continuous and categorical features.

Usage

data(Credit)

Format

A data frame with 653 observations, 15 predictors and a binary criterion variable called Response

Details

All observations with missing values are deleted.

Source

Frank, A. and Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

References

The original dataset can be downloaded at http://archive.ics.uci.edu/ml/datasets/Credit+Approval

Examples

data(Credit)
str(Credit)
table(Credit$Response)

Display the NEWS file

Description

kFNews shows the NEWS file of the kernelFactory package.

Usage

kFNews()

Value

None.

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

kernelFactory, predict.kernelFactory

Examples

kFNews()

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 (pol), LINear kernel function (lin), Radial Basis kernel Function rbf), random choice (random=pol, lin, rbf) (random), burn- in choice of best function (burn=pol, lin, rbf ) (burn). Use random or burn if you don't know in advance which kernel function is best.

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

predict.kernelFactory

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"])

Predict method for kernelFactory objects

Description

Prediction of new data using kernelFactory.

Usage

## S3 method for class 'kernelFactory'
predict(object, newdata = NULL, predict.all = FALSE,
  ...)

Arguments

object

An object of class kernelFactory, as created by the function kernelFactory

newdata

A data frame with the same predictors as in the training data.

predict.all

TRUE or FALSE. If TRUE and rp and cp are 1 then the individual predictions of the random forest are returned. If TRUE and any of rp and cp or bigger than 1 then the predictions of all the members are returned.

...

Not used currently.

Value

A vector containing the response probabilities.

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

kernelFactory

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"])

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