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

In symbolicDA: Analysis of Symbolic Data

Bagging algorithm for optimal split based on decision tree for symbolic objects

Description

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

Usage

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 [e-mail] Marcin Pełka [e-mail]

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: doi: 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

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

symbolicDA documentation built on Feb. 16, 2023, 6 p.m.