GAMens {GAMens} | R Documentation |
Fits the GAMbag, GAMrsm or GAMens ensemble algorithms for binary classification using generalized additive models as base classifiers.
GAMens(formula, data, rsm_size=2, autoform=FALSE, iter=10, df=4, bagging=TRUE, rsm=TRUE, fusion="avgagg")
formula |
a formula, as in the gam function. Smoothing splines are supported
as nonparametric smoothing terms, and should be indicated by s . See the documentation of s in the
gam package for its arguments. The GAMens function also provides the possibility for automatic
formula specification. See 'details' for more information. |
data |
a data frame in which to interpret the variables named in formula . |
rsm_size |
an integer, the number of variables to use for random feature subsets used in the Random Subspace Method. Default is 2.
If rsm=FALSE , the value of rsm_size is ignored. |
autoform |
if FALSE (default), the model specification in formula is used. If TRUE ,
the function triggers automatic formula specification. See 'details' for more information. |
iter |
an integer, the number of base classifiers (GAMs) in the ensemble. Defaults to iter=10
base classifiers. |
df |
an integer, the number of degrees of freedom (df) used for smoothing spline estimation. Its value
is only used when autoform = TRUE . Defaults to df=4 . Its value is ignored if a formula is
specified and autoform is FALSE . |
bagging |
enables Bagging if value is TRUE (default). If FALSE ,
Bagging is disabled. Either bagging , rsm or both should be TRUE |
rsm |
enables Random Subspace Method (RSM) if value is TRUE (default). If FALSE ,
RSM is disabled. Either bagging , rsm or both should be TRUE |
fusion |
specifies the fusion rule for the aggregation of member classifier outputs in the ensemble. Possible values are
'avgagg' (default), 'majvote' , 'w.avgagg' or 'w.majvote' . |
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.
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 GAMens object. |
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 GAMens object. |
bagging |
indicates whether bagging was used to create the GAMens object. |
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 fusion_method . |
Koen W. De Bock Koen.DeBock@UGent.be, Kristof Coussement K.Coussement@Ieseg.fr and Dirk Van den Poel Dirk.VandenPoel@UGent.be
De Bock, K. W., Coussement, K. and Van den Poel, D. (2010): "Ensemble Classification based on generalized additive models". Computational Statistics & Data Analysis, doi:10.1016/j.csda.2009.12.013.
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.
## Load data (mlbench library should be loaded) library(mlbench) data(Ionosphere) ## Train GAMens using all variables in Ionosphere dataset Ionosphere.GAMens <- GAMens(Class~., Ionosphere ,4 , autoform=TRUE, iter=20 ) ## 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)