Package weka.classifiers.trees.j48

Class BinC45ModelSelection

java.lang.Object

weka.classifiers.trees.j48.ModelSelection

weka.classifiers.trees.j48.BinC45ModelSelection

All Implemented Interfaces:

Serializable, RevisionHandler

public class BinC45ModelSelection
extends ModelSelection

Class for selecting a C4.5-like binary (!) split for a given dataset.

Version:

$Revision$

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor	Description
BinC45ModelSelection(int minNoObj, Instances allData, boolean useMDLcorrection, boolean doNotMakeSplitPointActualValue)	Initializes the split selection method with the given parameters.

Method Summary

Modifier and Type	Method	Description
void	cleanup()	Sets reference to training data to null.
String	getRevision()	Returns the revision string.
ClassifierSplitModel	selectModel(Instances data)	Selects C4.5-type split for the given dataset.
ClassifierSplitModel	selectModel(Instances train, Instances test)	Selects C4.5-type split for the given dataset.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

BinC45ModelSelection

public BinC45ModelSelection(int minNoObj,
                            Instances allData,
                            boolean useMDLcorrection,
                            boolean doNotMakeSplitPointActualValue)

Initializes the split selection method with the given parameters.

Parameters:

minNoObj - minimum number of instances that have to occur in at least two subsets induced by split

allData - FULL training dataset (necessary for selection of split points).

useMDLcorrection - whether to use MDL adjustement when finding splits on numeric attributes

Method Detail

cleanup

public void cleanup()

Sets reference to training data to null.

selectModel

public final ClassifierSplitModel selectModel(Instances data)
                                       throws Exception

Selects C4.5-type split for the given dataset.

Specified by:

selectModel in class ModelSelection

Throws:

Exception - if model can't be selected

selectModel

public final ClassifierSplitModel selectModel(Instances train,
                                              Instances test)
                                       throws Exception

Selects C4.5-type split for the given dataset.

Overrides:

selectModel in class ModelSelection

Throws:

Exception - if model can't be selected

getRevision

public String getRevision()

Returns the revision string.

Returns:

the revision