decisionTree.SDA: Decison tree for symbolic data

In symbolicDA: Analysis of Symbolic Data

Decison tree for symbolic data

Description

Optimal split based decision tree for symbolic objects

Usage

decisionTree.SDA(sdt,formula,testSet,treshMin=0.0001,treshW=-1e10,
tNodes=NULL,minSize=2,epsilon=1e-4,useEM=FALSE,
multiNominalType="ordinal",rf=FALSE,rf.size,objectSelection)

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
treshMin	parameter for tree creation algorithm
treshW	parameter for tree creation algorithm
tNodes	parameter for tree creation algorithm
minSize	parameter for tree creation algorithm
epsilon	parameter for tree creation algorithm
useEM	use Expectation Optimalization algorithm for estinating conditional probabilities
multiNominalType	"ordinal" - functione treats multi-nominal data as ordered or "nominal" functione treats multi-nomianal data as unordered (longer perfomance times)
rf	if TRUE symbolic variables for tree creation are randomly chosen like in random forest algorithm
rf.size	the number of variables chosen for tree creation if rf is true
objectSelection	optional, vector with symbolic object numbers for tree creation

Details

For futher details see ../doc/decisionTree_SDA.pdf

Value

nodes	nodes in tree
nodeObjects	contribution of each objects nodes in tree
conditionalProbab	conditional probability of belonginess of nodes te classes
prediction	predicted classes for objects from testSet

Author(s)

Andrzej Dudek [e-mail] Marcin Pelka [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,random.forest.SDA,draw.decisionTree.SDA

Examples

# Example 1
# LONG RUNNING - UNCOMMENT TO RUN
# File samochody.xml needed in this example 
# can be found in /inst/xml library of package
#sda<-parse.SO("samochody")
#tree<-decisionTree.SDA(sda, "Typ_samochodu~.", testSet=1:33)
#summary(tree) # a very gerneral information
#tree  # summary information

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