bagging.SDA: Bagging algorithm for optimal split based on decision tree...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Bagging algorithm for optimal split based on decision (classification) tree for symbolic objects

Usage

1
bagging.SDA(sdt,formula,testSet, mfinal=20,rf=FALSE,...) 

Arguments

sdt

Symbolic data table

formula

formula as in ln function

testSet

a vector of integers indicating classes to which each objects are allocated in learnig set

mfinal

number of partial models generated

rf

random forest like drawing of variables in partial models

...

arguments passed to decisionTree.SDA function

Details

The bagging, which stands for bootstrap aggregating, was introduced by Breiman in 1996. The diversity of classifiers in bagging is obtained by using bootstrapped replicas of the training data. Different training data subsets are randomly drawn with replacement from the entire training data set. Then each training data subset is used to train a decision tree (classifier). Individual classifiers are then combined by taking a simple majority vote of their decisions. For any given instance, the class chosen by most number of classifiers is the ensemble decision.

Value

An object of class bagging.SDA, which is a list with the following components:

predclass

the class predicted by the ensemble classifier

confusion

the confusion matrix for ensemble classifier

error

the classification error

pred

?

classfinal

final class memberships

Author(s)

Andrzej Dudek andrzej.dudek@ue.wroc.pl Marcin Pełka marcin.pelka@ue.wroc.pl

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/symbolicDA

References

Billard L., Diday E. (eds.) (2006), Symbolic Data Analysis, Conceptual Statistics and Data Mining, John Wiley & Sons, Chichester.

Bock H.H., Diday E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.

Breiman L. (1996), Bagging predictors, Machine Learning, vol. 24, no. 2, pp. 123-140. Available at: https://doi.org/10.1007/BF00058655.

Diday E., Noirhomme-Fraiture M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

See Also

boosting.SDA,random.forest.SDA,decisionTree.SDA

Examples

1
#Example will be available in next version of package, thank You for your patience :-)

symbolicDA documentation built on Aug. 8, 2019, 5:03 p.m.