GAMens {GAMens} | R Documentation |
Applies the GAMbag, GAMrsm or GAMens ensemble classifier to a data set
Description
Fits the GAMbag, GAMrsm or GAMens ensemble algorithms for binary classification using generalized additive models as base classifiers.
Usage
GAMens(formula, data, rsm_size = 2, autoform = FALSE, iter = 10, df = 4,
bagging = TRUE, rsm = TRUE, fusion = "avgagg")
Arguments
formula |
a formula, as in the |
data |
a data frame in which to interpret the variables named in
|
rsm_size |
an integer, the number of variables to use for random
feature subsets used in the Random Subspace Method. Default is 2. If
|
autoform |
if |
iter |
an integer, the number of base classifiers (GAMs) in the
ensemble. Defaults to |
df |
an integer, the number of degrees of freedom (df) used for
smoothing spline estimation. Its value is only used when |
bagging |
enables Bagging if value is |
rsm |
enables Random Subspace Method (RSM) if value is |
fusion |
specifies the fusion rule for the aggregation of member
classifier outputs in the ensemble. Possible values are |
Details
The GAMens
function applies the GAMbag, GAMrsm or GAMens ensemble
classifiers (De Bock et al., 2010) to a data set. GAMens is the default with
(bagging=TRUE
and rsm=TRUE
. For GAMbag, rsm
should be
specified as FALSE
. For GAMrsm, bagging
should be
FALSE
.
The GAMens
function provides the possibility for automatic formula
specification. In this case, dichotomous variables in data
are
included as linear terms, and other variables are assumed continuous,
included as nonparametric terms, and estimated by means of smoothing
splines. To enable automatic formula specification, use the generic formula
[response variable name]~.
in combination with autoform =
TRUE
. Note that in this case, all variables available in data
are
used in the model. If a formula other than [response variable name]~.
is specified then the autoform
option is automatically overridden. If
autoform=FALSE
and the generic formula [response variable
name]~.
is specified then the GAMs in the ensemble will not contain
nonparametric terms (i.e., will only consist of linear terms).
Four alternative fusion rules for member classifier outputs can be
specified. Possible values are 'avgagg'
for average aggregation
(default), 'majvote'
for majority voting, 'w.avgagg'
for
weighted average aggregation, or 'w.majvote'
for weighted majority
voting. Weighted approaches are based on member classifier error rates.
Value
An object of class GAMens
, which is a list with the following
components:
GAMs |
the member GAMs in the ensemble. |
formula |
the
formula used tot create the |
iter |
the ensemble size. |
df |
number of degrees of freedom (df) used for smoothing spline estimation. |
rsm |
indicates whether the Random
Subspace Method was used to create the |
bagging |
indicates whether bagging was used to create the
|
rsm_size |
the number of variables used for random feature subsets. |
fusion_method |
the fusion rule that was used to combine member classifier outputs in the ensemble. |
probs |
the class membership probabilities, predicted by the ensemble classifier. |
class |
the class predicted by the ensemble classifier. |
samples |
an array indicating, for every base classifier in the ensemble, which observations were used for training. |
weights |
a
vector with weights defined as (1 - error rate). Usage depends upon
specification of |
Author(s)
Koen W. De Bock kdebock@audencia.com, Kristof Coussement K.Coussement@ieseg.fr and Dirk Van den Poel Dirk.VandenPoel@ugent.be
References
De Bock, K.W. and Van den Poel, D. (2012): "Reconciling Performance and Interpretability in Customer Churn Prediction Modeling Using Ensemble Learning Based on Generalized Additive Models". Expert Systems With Applications, Vol 39, 8, pp. 6816–6826.
De Bock, K. W., Coussement, K. and Van den Poel, D. (2010): "Ensemble Classification based on generalized additive models". Computational Statistics & Data Analysis, Vol 54, 6, pp. 1535–1546.
Breiman, L. (1996): "Bagging predictors". Machine Learning, Vol 24, 2, pp. 123–140.
Hastie, T. and Tibshirani, R. (1990): "Generalized Additive Models", Chapman and Hall, London.
Ho, T. K. (1998): "The random subspace method for constructing decision forests". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 20, 8, pp. 832–844.
See Also
Examples
## Load data (mlbench library should be loaded)
library(mlbench)
data(Ionosphere)
IonosphereSub<-Ionosphere[,c("V1","V2","V3","V4","V5","Class")]
## Train GAMens using all variables in Ionosphere dataset
Ionosphere.GAMens <- GAMens(Class~., IonosphereSub ,4 , autoform=TRUE,
iter=10 )
## Compare classification performance of GAMens, GAMrsm and GAMbag ensembles,
## using 4 nonparametric terms and 2 linear terms
Ionosphere.GAMens <- GAMens(Class~s(V3,4)+s(V4,4)+s(V5,3)+s(V6,5)+V7+V8,
Ionosphere ,3 , autoform=FALSE, iter=10 )
Ionosphere.GAMrsm <- GAMens(Class~s(V3,4)+s(V4,4)+s(V5,3)+s(V6,5)+V7+V8,
Ionosphere ,3 , autoform=FALSE, iter=10, bagging=FALSE, rsm=TRUE )
Ionosphere.GAMbag <- GAMens(Class~s(V3,4)+s(V4,4)+s(V5,3)+s(V6,5)+V7+V8,
Ionosphere ,3 , autoform=FALSE, iter=10, bagging=TRUE, rsm=FALSE )
## Calculate AUCs (for function colAUC, load caTools library)
library(caTools)
GAMens.auc <- colAUC(Ionosphere.GAMens[[9]], Ionosphere["Class"]=="good",
plotROC=FALSE)
GAMrsm.auc <- colAUC(Ionosphere.GAMrsm[[9]], Ionosphere["Class"]=="good",
plotROC=FALSE)
GAMbag.auc <- colAUC(Ionosphere.GAMbag[[9]], Ionosphere["Class"]=="good",
plotROC=FALSE)