weka.classifiers.j48
Class PART

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier
              |
              +--weka.classifiers.j48.PART

All Implemented Interfaces:

AdditionalMeasureProducer, java.lang.Cloneable, OptionHandler, java.io.Serializable, Summarizable, WeightedInstancesHandler

public class PART

extends DistributionClassifier

implements OptionHandler, WeightedInstancesHandler, Summarizable, AdditionalMeasureProducer

Class for generating a PART decision list. For more information, see

Eibe Frank and Ian H. Witten (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA.

Valid options are:

-C confidence
Set confidence threshold for pruning. (Default: 0.25)

-M number
Set minimum number of instances per leaf. (Default: 2)

-R
Use reduced error pruning.

-N number
Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-B
Use binary splits for nominal attributes.

Author:

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

See Also:

Serialized Form

Constructor Summary

PART()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Classifies an instance.
double[]	distributionForInstance(Instance instance) Returns class probabilities for an instance.
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
boolean	getBinarySplits() Get the value of binarySplits.
float	getConfidenceFactor() Get the value of CF.
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
int	getMinNumObj() Get the value of minNumObj.
int	getNumFolds() Get the value of numFolds.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
boolean	getReducedErrorPruning() Get the value of reducedErrorPruning.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options Valid options are:
static void	main(java.lang.String[] argv) Main method for testing this class.
double	measureNumRules() Return the number of rules.
void	setBinarySplits(boolean v) Set the value of binarySplits.
void	setConfidenceFactor(float v) Set the value of CF.
void	setMinNumObj(int v) Set the value of minNumObj.
void	setNumFolds(int v) Set the value of numFolds.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setReducedErrorPruning(boolean v) Set the value of reducedErrorPruning.
java.lang.String	toString() Returns a description of the classifier
java.lang.String	toSummaryString() Returns a superconcise version of the model

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

PART

public PART()

Method Detail

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Overrides:

buildClassifier in class Classifier

Throws:

java.lang.Exception - if classifier can't be built successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Classifies an instance.

Overrides:

classifyInstance in class DistributionClassifier

Throws:

java.lang.Exception - if instance can't be classified successfully

distributionForInstance

public final double[] distributionForInstance(Instance instance)
                                       throws java.lang.Exception

Returns class probabilities for an instance.

Overrides:

distributionForInstance in class DistributionClassifier

Throws:

java.lang.Exception - if the distribution can't be computed successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options Valid options are:

-C confidence
Set confidence threshold for pruning. (Default: 0.25)

-M number
Set minimum number of instances per leaf. (Default: 2)

-R
Use reduced error pruning.

-N number
Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-B
Use binary splits for nominal attributes.

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

toString

public java.lang.String toString()

Returns a description of the classifier

Overrides:

toString in class java.lang.Object

toSummaryString

public java.lang.String toSummaryString()

Returns a superconcise version of the model

Specified by:

toSummaryString in interface Summarizable

Following copied from interface: weka.core.Summarizable

Returns:

the object summarized as a string

measureNumRules

public double measureNumRules()

Return the number of rules.

Returns:

the number of rules

enumerateMeasures

public java.util.Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(java.lang.String additionalMeasureName)

Returns the value of the named measure

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

measureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

java.lang.IllegalArgumentException - if the named measure is not supported

getConfidenceFactor

public float getConfidenceFactor()

Get the value of CF.

Returns:

Value of CF.

setConfidenceFactor

public void setConfidenceFactor(float v)

Set the value of CF.

Parameters:

v - Value to assign to CF.

getMinNumObj

public int getMinNumObj()

Get the value of minNumObj.

Returns:

Value of minNumObj.

setMinNumObj

public void setMinNumObj(int v)

Set the value of minNumObj.

Parameters:

v - Value to assign to minNumObj.

getReducedErrorPruning

public boolean getReducedErrorPruning()

Get the value of reducedErrorPruning.

Returns:

Value of reducedErrorPruning.

setReducedErrorPruning

public void setReducedErrorPruning(boolean v)

Set the value of reducedErrorPruning.

Parameters:

v - Value to assign to reducedErrorPruning.

getNumFolds

public int getNumFolds()

Get the value of numFolds.

Returns:

Value of numFolds.

setNumFolds

public void setNumFolds(int v)

Set the value of numFolds.

Parameters:

v - Value to assign to numFolds.

getBinarySplits

public boolean getBinarySplits()

Get the value of binarySplits.

Returns:

Value of binarySplits.

setBinarySplits

public void setBinarySplits(boolean v)

Set the value of binarySplits.

Parameters:

v - Value to assign to binarySplits.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

String - options