boosting.SDA: Boosting algorithm for optimal split based decision tree for...

boosting.SDAR Documentation

Boosting algorithm for optimal split based decision tree for symbolic objects

Description

Boosting algorithm for optimal split based decision tree for symbolic objects, "symbolic" version of adabag.M1 algorithm

Usage

boosting.SDA(sdt,formula,testSet, mfinal = 20,...) 

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

...

arguments passed to decisionTree.SDA function

Details

Boosting, similar to bagging, also creates an ensemble of classifiers by resampling the data. The results are then combined by majority voting. Resampling in boosting provides the most informative training data for each consecutive classifier. In each iteration of boosting three weak classifiers are created: the first classifier C1 is trained with a random subset of the training data. The training data subset for the next classifier C2 is chosen as the most informative subset, given C1.C2 is trained on a training data only half of wich is correctly classified by C1 and the other half is misclassified. The third classifier C3 is trained with instances on which C1 and C2 disagree. Then the three classifiers are combined through a three-way majority vote.

Value

formula

a symbolic description of the model that was used

trees

trees built whlie making the ensemble

weights

weights for each object from test set

votes

final consensus clustering

class

predicted class memberships

error

error rate of the ensemble clustering

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.

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

See Also

bagging.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 May 28, 2022, 1:08 a.m.