weka.classifiers
Class AdaBoostM1

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier
              |
              +--weka.classifiers.AdaBoostM1

All Implemented Interfaces:

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

public class AdaBoostM1

extends DistributionClassifier

implements OptionHandler, 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.

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 Classifier	m_Classifier The model base classifier to use
protected Classifier[]	m_Classifiers Array for storing the generated base classifiers.
protected boolean	m_Debug Debugging mode, gives extra output if true
protected int	m_MaxIterations The maximum number of boost iterations
protected int	m_NumClasses The number of classes
protected int	m_NumIterations The number of successfully generated base classifiers.
protected int	m_Seed Seed for boosting with resampling.
protected boolean	m_UseResampling Use boosting with reweighting?
protected int	m_WeightThreshold Weight Threshold.

Constructor Summary

AdaBoostM1()

Method Summary

void	buildClassifier(Instances data) Boosting method.
protected void	buildClassifierUsingResampling(Instances data) Boosting method.
protected void	buildClassifierWithWeights(Instances data) Boosting method.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
Classifier	getClassifier() Get the classifier used as the classifier
boolean	getDebug() Get whether debugging is turned on
int	getMaxIterations() Get the maximum number of boost iterations
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
int	getSeed() Get seed for resampling.
boolean	getUseResampling() Get whether resampling is turned on
int	getWeightThreshold() Get the degree of weight thresholding
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	setClassifier(Classifier newClassifier) Set the classifier for boosting.
void	setDebug(boolean debug) Set debugging mode
void	setMaxIterations(int maxIterations) Set the maximum number of boost iterations
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int seed) Set seed for resampling.
void	setUseResampling(boolean r) Set resampling mode
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.

Methods inherited from class weka.classifiers.DistributionClassifier

classifyInstance

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_Classifier

protected Classifier m_Classifier

The model base classifier to use

m_Classifiers

protected Classifier[] m_Classifiers

Array for storing the generated base classifiers.

m_Betas

protected double[] m_Betas

Array for storing the weights for the votes.

m_MaxIterations

protected int m_MaxIterations

The maximum number of boost iterations

m_NumIterations

protected int m_NumIterations

The number of successfully generated base classifiers.

m_WeightThreshold

protected int m_WeightThreshold

Weight Threshold. The percentage of weight mass used in training

m_Debug

protected boolean m_Debug

Debugging mode, gives extra output if true

m_UseResampling

protected boolean m_UseResampling

Use boosting with reweighting?

m_Seed

protected int m_Seed

Seed for boosting with resampling.

m_NumClasses

protected int m_NumClasses

The number of classes

Constructor Detail

AdaBoostM1

public AdaBoostM1()

Method Detail

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

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

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

Returns:

an array of strings suitable for passing to setOptions

setClassifier

public void setClassifier(Classifier newClassifier)

Set the classifier for boosting.

Parameters:

newClassifier - the Classifier to use.

getClassifier

public Classifier getClassifier()

Get the classifier used as the classifier

Returns:

the classifier used as the classifier

setMaxIterations

public void setMaxIterations(int maxIterations)

Set the maximum number of boost iterations

getMaxIterations

public int getMaxIterations()

Get the maximum number of boost iterations

Returns:

the maximum number of boost iterations

setWeightThreshold

public void setWeightThreshold(int threshold)

Set weight threshold

Parameters:

thresholding - 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

setSeed

public void setSeed(int seed)

Set seed for resampling.

Parameters:

seed - the seed for resampling

getSeed

public int getSeed()

Get seed for resampling.

Returns:

the seed for resampling

setDebug

public void setDebug(boolean debug)

Set debugging mode

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Get whether debugging is turned on

Returns:

true if debugging output is on

setUseResampling

public void setUseResampling(boolean r)

Set resampling mode

Parameters:

resampling - true if resampling should be done

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 Classifier

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

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 DistributionClassifier

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

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.

Overrides:

toString in class java.lang.Object

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