MultiBoostAB

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

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