Package 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:
Serializable, 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:

  • Constructor Details

    • MultiBoostAB

      public MultiBoostAB()

  • Method Details

    • globalInfo

      public 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 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(String[] options) throws 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:
      Exception - if an option is not supported

    • getOptions

      public 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 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 Exception
      Method for building this classifier.
      Overrides:
      buildClassifier in class AdaBoostM1
      Parameters:
      training - the data to train with
      Throws:
      Exception - if the training fails

    • toString

      public String toString()
      Returns description of the boosted classifier.
      Overrides:
      toString in class AdaBoostM1
      Returns:
      description of the boosted classifier as a string

    • getRevision

      public 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(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - the options