weka.classifiers.meta
Class RealAdaBoost

java.lang.Object
  extended by weka.classifiers.AbstractClassifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.IteratedSingleClassifierEnhancer
              extended by weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  extended by weka.classifiers.meta.RealAdaBoost
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class RealAdaBoost
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, TechnicalInformationHandler

Class for boosting a 2-class classifier using the Real Adaboost method.

For more information, see

J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.

BibTeX:

 @article{Friedman2000,
    author = {J. Friedman and T. Hastie and R. Tibshirani},
    journal = {Annals of Statistics},
    number = {2},
    pages = {337-407},
    title = {Additive Logistic Regression: a Statistical View of Boosting},
    volume = {95},
    year = {2000}
 }
 

Valid options are:

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 -Q
  Use resampling for boosting.
 -H <num>
  Shrinkage parameter.
  (default 1)
 -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: 6136 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
RealAdaBoost()
          Constructor.
 
Method Summary
 void buildClassifier(Instances data)
          Boosting method.
 double[] distributionForInstance(Instance instance)
          Calculates the class membership probabilities for the given test instance.
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 java.lang.String getRevision()
          Returns the revision string.
 double getShrinkage()
          Get the value of Shrinkage.
 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.
 boolean getUseResampling()
          Get whether resampling is turned on
 int getWeightThreshold()
          Get the degree of weight thresholding
 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.
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setShrinkage(double newShrinkage)
          Set the value of Shrinkage.
 void setUseResampling(boolean r)
          Set resampling mode
 void setWeightThreshold(int threshold)
          Set weight threshold
 java.lang.String shrinkageTipText()
          Returns the tip text for this property
 java.lang.String toString()
          Returns description of the boosted classifier.
 java.lang.String useResamplingTipText()
          Returns the tip text for this property
 java.lang.String weightThresholdTipText()
          Returns the tip text for this property
 
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.AbstractClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

RealAdaBoost

public RealAdaBoost()
Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing classifier

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
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 RandomizableIteratedSingleClassifierEnhancer
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:

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 -Q
  Use resampling for boosting.
 -H <num>
  Shrinkage parameter.
  (default 1)
 -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 RandomizableIteratedSingleClassifierEnhancer
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 RandomizableIteratedSingleClassifierEnhancer
Returns:
an array of strings suitable for passing to setOptions

shrinkageTipText

public java.lang.String shrinkageTipText()
Returns the tip text for this property

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

getShrinkage

public double getShrinkage()
Get the value of Shrinkage.

Returns:
Value of Shrinkage.

setShrinkage

public void setShrinkage(double newShrinkage)
Set the value of Shrinkage.

Parameters:
newShrinkage - Value to assign to Shrinkage.

weightThresholdTipText

public java.lang.String weightThresholdTipText()
Returns the tip text for this property

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

setWeightThreshold

public void setWeightThreshold(int threshold)
Set weight threshold

Parameters:
threshold - the percentage of weight mass used for training

getWeightThreshold

public int getWeightThreshold()
Get the degree of weight thresholding

Returns:
the percentage of weight mass used for training

useResamplingTipText

public java.lang.String useResamplingTipText()
Returns the tip text for this property

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

setUseResampling

public void setUseResampling(boolean r)
Set resampling mode

Parameters:
r - true if resampling should be done

getUseResampling

public boolean getUseResampling()
Get whether resampling is turned on

Returns:
true if resampling output is on

getCapabilities

public Capabilities getCapabilities()
Returns default capabilities of the classifier.

Specified by:
getCapabilities in interface Classifier
Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class SingleClassifierEnhancer
Returns:
the capabilities of this classifier
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception
Boosting method.

Specified by:
buildClassifier in interface Classifier
Overrides:
buildClassifier in class IteratedSingleClassifierEnhancer
Parameters:
data - the training data to be used for generating the boosted classifier.
Throws:
java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.

Specified by:
distributionForInstance in interface Classifier
Overrides:
distributionForInstance in class AbstractClassifier
Parameters:
instance - the instance to be classified
Returns:
predicted class probability distribution
Throws:
java.lang.Exception - if instance could not be classified successfully

toString

public java.lang.String toString()
Returns description of the boosted classifier.

Overrides:
toString in class java.lang.Object
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 AbstractClassifier
Returns:
the revision

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options