weka.classifiers.meta
Class AdaBoostM1

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.IteratedSingleClassifierEnhancer
              weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  weka.classifiers.meta.AdaBoostM1

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable, Sourcable, WeightedInstancesHandler

Direct Known Subclasses:

MultiBoostAB

public class AdaBoostM1

extends RandomizableIteratedSingleClassifierEnhancer

implements WeightedInstancesHandler, Sourcable

Class for boosting a classifier using Freund & Schapire's Adaboost M1 method. For more information, see

Yoav Freund and Robert E. Schapire (1996). Experiments with a new boosting algorithm. Proc International Conference on Machine Learning, pages 148-156, Morgan Kaufmann, San Francisco.

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a classifier as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.23 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected double[]	m_Betas Array for storing the weights for the votes.
protected int	m_NumClasses The number of classes
protected int	m_NumIterationsPerformed The number of successfully generated base classifiers.
protected boolean	m_UseResampling Use boosting with reweighting?
protected int	m_WeightThreshold Weight Threshold.
private static int	MAX_NUM_RESAMPLING_ITERATIONS Max num iterations tried to find classifier with non-zero error.

Fields inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

m_Seed

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

m_Classifiers, m_NumIterations

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

m_Classifier

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

AdaBoostM1()
Constructor.

Method Summary

void	buildClassifier(Instances data) Boosting method.
protected void	buildClassifierUsingResampling(Instances data) Boosting method.
protected void	buildClassifierWithWeights(Instances data) Boosting method.
protected java.lang.String	defaultClassifierString() String describing default classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
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.
protected Instances	selectWeightQuantile(Instances data, double quantile) Select only instances with weights that contribute to the specified quantile of the weight distribution
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseResampling(boolean r) Set resampling mode
protected void	setWeights(Instances training, double reweight) Sets the weights for the next iteration.
void	setWeightThreshold(int threshold) Set weight threshold
java.lang.String	toSource(java.lang.String className) Returns the boosted model as Java source code.
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, getClassifierSpec, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

MAX_NUM_RESAMPLING_ITERATIONS

private static int MAX_NUM_RESAMPLING_ITERATIONS

Max num iterations tried to find classifier with non-zero error.

m_Betas

protected double[] m_Betas

Array for storing the weights for the votes.

m_NumIterationsPerformed

protected int m_NumIterationsPerformed

The number of successfully generated base classifiers.

m_WeightThreshold

protected int m_WeightThreshold

Weight Threshold. The percentage of weight mass used in training

m_UseResampling

protected boolean m_UseResampling

Use boosting with reweighting?

m_NumClasses

protected int m_NumClasses

The number of classes

Constructor Detail

AdaBoostM1

public AdaBoostM1()

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

defaultClassifierString

protected java.lang.String defaultClassifierString()

String describing default classifier.

Overrides:

defaultClassifierString in class SingleClassifierEnhancer

selectWeightQuantile

protected Instances selectWeightQuantile(Instances data,
                                         double quantile)

Select only instances with weights that contribute to the specified quantile of the weight distribution

Parameters:

data - the input instances

quantile - the specified quantile eg 0.9 to select 90% of the weight mass

Returns:

the selected instances

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:

-D
Turn on debugging output.

-W classname
Specify the full class name of a classifier as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

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

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

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

getUseResampling

public boolean getUseResampling()

Get whether resampling is turned on

Returns:

true if resampling output is on

buildClassifier

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

Boosting method.

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

buildClassifierUsingResampling

protected void buildClassifierUsingResampling(Instances data)
                                       throws java.lang.Exception

Boosting method. Boosts using resampling

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

setWeights

protected void setWeights(Instances training,
                          double reweight)
                   throws java.lang.Exception

Sets the weights for the next iteration.

Throws:

java.lang.Exception

buildClassifierWithWeights

protected void buildClassifierWithWeights(Instances data)
                                   throws java.lang.Exception

Boosting method. Boosts any classifier that can handle weighted instances.

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.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if instance could not be classified successfully

toSource

public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception

Returns the boosted model as Java source code.

Specified by:

toSource in interface Sourcable

Parameters:

className - the name that should be given to the source class.

Returns:

the tree as Java source code

Throws:

java.lang.Exception - if something goes wrong

toString

public java.lang.String toString()

Returns description of the boosted classifier.

Returns:

description of the boosted classifier as a string

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options