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

Constructors
Constructor and Description

MultiBoostAB()

Constructors
Constructor and Description
`MultiBoostAB()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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

Class MultiBoostAB

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.meta.AdaBoostM1

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

MultiBoostAB

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

numSubCmtysTipText

setNumSubCmtys

getNumSubCmtys

buildClassifier

toString

getRevision

main