random.forest.SDA: Random forest algorithm for optimal split based decision tree...

In symbolicDA: Analysis of Symbolic Data

Random forest algorithm for optimal split based decision tree for symbolic objects

Description

Random forest algorithm for optimal split based decision tree for symbolic objects

Usage

random.forest.SDA(sdt,formula,testSet, mfinal = 100,...)

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

random.forest.SDA implements Breiman's random forest algorithm for classification of symbolic data set.

Value

Section details goes here

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.

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

See Also

bagging.SDA,boosting.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.