weka.classifiers.meta
Class MultiBoostAB

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.IteratedSingleClassifierEnhancer
              weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  weka.classifiers.meta.AdaBoostM1
                      weka.classifiers.meta.MultiBoostAB

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable, Sourcable, WeightedInstancesHandler

public class MultiBoostAB

extends AdaBoostM1

Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.
For more information, see

Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning, 40(2): 159-196, Kluwer Academic Publishers, Boston

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a classifier as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-C subcommittees
Number of sub-committees. (Default 3),

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.5 $ `

Author:

Shane Butler (sbutle@deakin.edu.au), Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected int	m_NumSubCmtys The number of sub-committees to use
protected java.util.Random	m_Random Random number generator

Fields inherited from class weka.classifiers.meta.AdaBoostM1

m_Betas, m_NumClasses, m_NumIterationsPerformed, m_UseResampling, m_WeightThreshold

Fields inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

m_Seed

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

m_Classifiers, m_NumIterations

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

m_Classifier

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

MultiBoostAB()

Method Summary

void	buildClassifier(Instances training) Method for building this classifier.
int	getNumSubCmtys() Get the number of sub committees to use
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	numSubCmtysTipText() Returns the tip text for this property
void	setNumSubCmtys(int subc) Set the number of sub committees to use
void	setOptions(java.lang.String[] options) Parses a given list of options.
protected void	setWeights(Instances training, double reweight) Sets the weights for the next iteration.
java.lang.String	toString() Returns description of the boosted classifier.

Methods inherited from class weka.classifiers.meta.AdaBoostM1

buildClassifierUsingResampling, buildClassifierWithWeights, defaultClassifierString, distributionForInstance, getUseResampling, getWeightThreshold, selectWeightQuantile, setUseResampling, setWeightThreshold, toSource, useResamplingTipText, weightThresholdTipText

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, getClassifierSpec, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_NumSubCmtys

protected int m_NumSubCmtys

The number of sub-committees to use

m_Random

protected java.util.Random m_Random

Random number generator

Constructor Detail

MultiBoostAB

public MultiBoostAB()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Overrides:

globalInfo in class AdaBoostM1

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AdaBoostM1

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. Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a classifier as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-C subcommittees
Number of sub-committees. (Default 3),

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AdaBoostM1

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

Overrides:

getOptions in class AdaBoostM1

Returns:

an array of strings suitable for passing to setOptions

numSubCmtysTipText

public java.lang.String numSubCmtysTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumSubCmtys

public void setNumSubCmtys(int subc)

Set the number of sub committees to use

getNumSubCmtys

public int getNumSubCmtys()

Get the number of sub committees to use

Returns:

the seed for resampling

buildClassifier

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

Method for building this classifier.

Overrides:

buildClassifier in class AdaBoostM1

Parameters:

training - the training data to be used for generating the boosted classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

setWeights

protected void setWeights(Instances training,
                          double reweight)
                   throws java.lang.Exception

Sets the weights for the next iteration.

Overrides:

setWeights in class AdaBoostM1

Throws:

java.lang.Exception

toString

public java.lang.String toString()

Returns description of the boosted classifier.

Overrides:

toString in class AdaBoostM1

Returns:

description of the boosted classifier as a string

main

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

Main method for testing this class.

Parameters:

argv - the options