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.

Version:: $Revision: 15520 $
Author:: Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

AdaBoostM1()
Constructor.

Constructors
Constructor and Description
`AdaBoostM1()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Method used to build the 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.
`boolean`	`getResume()` Returns true if the model is to be finalized (or has been finalized) after training.
`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.
`java.lang.String`	`resumeTipText()` Tool tip text for the resume property
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setResume(boolean resume)` If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
`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.
`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, 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
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.classifiers.Classifier
classifyInstance

Methods inherited from interface weka.core.OptionHandler
makeCopy

- 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
  - 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
  - resumeTipText
```
public java.lang.String resumeTipText()
```
    Tool tip text for the resume property
    
    Returns:
    
    the tool tip text for the finalize property
  - setResume
```
public void setResume(boolean resume)
```
    If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
    
    Specified by:
    
    setResume in interface IterativeClassifier
    
    Parameters:
    
    resume - true if the model is to be finalized after performing iterations
  - getResume
```
public boolean getResume()
```
    Returns true if the model is to be finalized (or has been finalized) after training.
    
    Specified by:
    
    getResume in interface IterativeClassifier
    
    Returns:
    
    the current value of finalize
  - 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

Class AdaBoostM1

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.classifiers.Classifier

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

AdaBoostM1

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

weightThresholdTipText

setWeightThreshold

getWeightThreshold

useResamplingTipText

setUseResampling

getUseResampling

getCapabilities

buildClassifier

initializeClassifier

next

done

resumeTipText

setResume

getResume

distributionForInstance

toSource

toString

getRevision

main