weka.classifiers.meta
Class RealAdaBoost

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.IteratedSingleClassifierEnhancer
              weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  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