weka.classifiers.meta

Class AdaBoostM1

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

weka.classifiers.meta.AdaBoostM1

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, Sourcable, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class AdaBoostM1
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, Sourcable, TechnicalInformationHandler, IterativeClassifier

Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.

For more information, see

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

BibTeX:

 @inproceedings{Freund1996,
    address = {San Francisco},
    author = {Yoav Freund and Robert E. Schapire},
    booktitle = {Thirteenth International Conference on Machine Learning},
    pages = {148-156},
    publisher = {Morgan Kaufmann},
    title = {Experiments with a new boosting algorithm},
    year = {1996}
 }
 

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.
 

 -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.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
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 java.util.Random	m_RandomInstance Random number generator to be used for resampling
protected Instances	m_TrainingData The (weighted) training data
protected boolean	m_UseResampling Use boosting with reweighting?
protected int	m_WeightThreshold Weight Threshold.
protected Classifier	m_ZeroR a ZeroR model in case no model can be built from the data

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.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
AdaBoostM1() Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Method used to build the classifier.
protected java.lang.String	defaultClassifierString() String describing default classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
void	done() Clean up after boosting.
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.
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
void	initializeClassifier(Instances data) Initialize the classifier.
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
boolean	next() Perform the next boosting iteration.
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

defaultNumberOfIterations, getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.classifiers.Classifier

classifyInstance

Field Detail

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

m_ZeroR

protected Classifier m_ZeroR

a ZeroR model in case no model can be built from the data

m_TrainingData

protected Instances m_TrainingData

The (weighted) training data

m_RandomInstance

protected java.util.Random m_RandomInstance

Random number generator to be used for resampling

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

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

defaultClassifierString

protected java.lang.String defaultClassifierString()

String describing default classifier.

Overrides:

defaultClassifierString in class SingleClassifierEnhancer

Returns:

the default classifier classname

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<Option> 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.
 

 -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

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

Method used to build the classifier.

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

Parameters:

data - the training data to be used for generating the bagged classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

initializeClassifier

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

Initialize the classifier.

Specified by:

initializeClassifier in interface IterativeClassifier

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

next

public boolean next()
             throws java.lang.Exception

Perform the next boosting iteration.

Specified by:

next in interface IterativeClassifier

Returns:

false if no further iterations could be performed, true otherwise

Throws:

java.lang.Exception - if an unforeseen problem occurs

done

public void done()

Clean up after boosting.

Specified by:

done in interface IterativeClassifier

setWeights

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

Sets the weights for the next iteration.

Parameters:

training - the training instances

reweight - the reweighting factor

Throws:

java.lang.Exception - if something goes wrong

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

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 classname of the generated 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.

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