weka.classifiers.meta
Class AdaBoostM1

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.IteratedSingleClassifierEnhancer
              extended by weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  extended by weka.classifiers.meta.AdaBoostM1
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Sourcable, CapabilitiesHandler, OptionHandler, Randomizable, TechnicalInformationHandler, WeightedInstancesHandler
Direct Known Subclasses:
MultiBoostAB

public class AdaBoostM1
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, Sourcable, TechnicalInformationHandler

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: 1.39 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
AdaBoostM1()
          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.
 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 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, setClassifier
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

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 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 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.

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.

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 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

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options