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.io.Serializable, java.lang.Cloneable, Sourcable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class MultiBoostAB
extends AdaBoostM1
implements TechnicalInformationHandler

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. Vol.40(No.2).

BibTeX:

 @article{Webb2000,
    address = {Boston},
    author = {Geoffrey I. Webb},
    journal = {Machine Learning},
    number = {No.2},
    publisher = {Kluwer Academic Publishers},
    title = {MultiBoosting: A Technique for Combining Boosting and Wagging},
    volume = {Vol.40},
    year = {2000}
 }
 

Valid options are:

 -C <num>
  Number of sub-committees. (Default 3)

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -Q
  Use resampling for boosting.

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.16 $

Author:

Shane Butler ([email protected]), Eibe Frank ([email protected]), Len Trigg ([email protected])

See Also:

Serialized Form

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	getRevision() Returns the revision string.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
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.
java.lang.String	toString() Returns description of the boosted classifier.

Methods inherited from class weka.classifiers.meta.AdaBoostM1

distributionForInstance, getCapabilities, getUseResampling, getWeightThreshold, 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, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object

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

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

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Overrides:

getTechnicalInformation in class AdaBoostM1

Returns:

the technical information about this class

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:

 -C <num>
  Number of sub-committees. (Default 3)

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -Q
  Use resampling for boosting.

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

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

Parameters:

subc - the number of sub committees

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 data to train with

Throws:

java.lang.Exception - if the training fails

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

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AdaBoostM1

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - the options