weka.classifiers.meta
Class Decorate

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.Decorate
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, TechnicalInformationHandler

public class Decorate
extends RandomizableIteratedSingleClassifierEnhancer
implements TechnicalInformationHandler

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests.Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets.

For more details see:

P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003.

P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems..

BibTeX:

 @inproceedings{Melville2003,
    author = {P. Melville and R. J. Mooney},
    booktitle = {Eighteenth International Joint Conference on Artificial Intelligence},
    pages = {505-510},
    title = {Constructing Diverse Classifier Ensembles Using Artificial Training Examples},
    year = {2003}
 }
 
 @article{Melville2004,
    author = {P. Melville and R. J. Mooney},
    journal = {Information Fusion: Special Issue on Diversity in Multiclassifier Systems},
    note = {submitted},
    title = {Creating Diversity in Ensembles Using Artificial Data},
    year = {2004}
 }
 

Valid options are:

 -E
  Desired size of ensemble.
  (default 10)
 -R
  Factor that determines number of artificial examples to generate.
  Specified proportional to training set size.
  (default 1.0)
 -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.J48)
 
 Options specific to classifier weka.classifiers.trees.J48:
 
 -U
  Use unpruned tree.
 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 -R
  Use reduced error pruning.
 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 -B
  Use binary splits only.
 -S
  Don't perform subtree raising.
 -L
  Do not clean up after the tree has been built.
 -A
  Laplace smoothing for predicted probabilities.
 -Q <seed>
  Seed for random data shuffling (default 1).
Options after -- are passed to the designated classifier.

Version:
$Revision: 1.8 $
Author:
Prem Melville (melville@cs.utexas.edu)
See Also:
Serialized Form

Constructor Summary
Decorate()
          Constructor.
 
Method Summary
 java.lang.String artificialSizeTipText()
          Returns the tip text for this property
 void buildClassifier(Instances data)
          Build Decorate classifier
 java.lang.String desiredSizeTipText()
          Returns the tip text for this property
 double[] distributionForInstance(Instance instance)
          Calculates the class membership probabilities for the given test instance.
 double getArtificialSize()
          Factor that determines number of artificial examples to generate.
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 int getDesiredSize()
          Gets the desired size of the committee.
 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.
 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.
 java.lang.String numIterationsTipText()
          Returns the tip text for this property
 void setArtificialSize(double newArtSize)
          Sets factor that determines number of artificial examples to generate.
 void setDesiredSize(int newDesiredSize)
          Sets the desired size of the committee.
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 java.lang.String toString()
          Returns description of the Decorate classifier.
 
Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
 
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, 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

Decorate

public Decorate()
Constructor.

Method Detail

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:

 -E
  Desired size of ensemble.
  (default 10)
 -R
  Factor that determines number of artificial examples to generate.
  Specified proportional to training set size.
  (default 1.0)
 -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.J48)
 
 Options specific to classifier weka.classifiers.trees.J48:
 
 -U
  Use unpruned tree.
 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 -R
  Use reduced error pruning.
 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 -B
  Use binary splits only.
 -S
  Don't perform subtree raising.
 -L
  Do not clean up after the tree has been built.
 -A
  Laplace smoothing for predicted probabilities.
 -Q <seed>
  Seed for random data shuffling (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

desiredSizeTipText

public java.lang.String desiredSizeTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

numIterationsTipText

public java.lang.String numIterationsTipText()
Returns the tip text for this property

Overrides:
numIterationsTipText in class IteratedSingleClassifierEnhancer
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

artificialSizeTipText

public java.lang.String artificialSizeTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

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

getArtificialSize

public double getArtificialSize()
Factor that determines number of artificial examples to generate.

Returns:
factor that determines number of artificial examples to generate

setArtificialSize

public void setArtificialSize(double newArtSize)
Sets factor that determines number of artificial examples to generate.

Parameters:
newArtSize - factor that determines number of artificial examples to generate

getDesiredSize

public int getDesiredSize()
Gets the desired size of the committee.

Returns:
the desired size of the committee

setDesiredSize

public void setDesiredSize(int newDesiredSize)
Sets the desired size of the committee.

Parameters:
newDesiredSize - the desired size of the committee

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
Build Decorate classifier

Overrides:
buildClassifier in class IteratedSingleClassifierEnhancer
Parameters:
data - the training data to be used for generating the 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 distribution can't be computed successfully

toString

public java.lang.String toString()
Returns description of the Decorate classifier.

Overrides:
toString in class java.lang.Object
Returns:
description of the Decorate classifier as a string

main

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

Parameters:
argv - the options