Decorate

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.IteratedSingleClassifierEnhancer
    - - weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
      - weka.classifiers.meta.Decorate

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, 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 15)

 -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 50)

 -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: 8037 $
Author:: Prem Melville (melville@cs.utexas.edu)
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

Decorate()
Constructor.

Constructors
Constructor and Description
`Decorate()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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.
`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.
`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 15)
```
```
 -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 50)
```
```
 -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
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class Decorate

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.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

Decorate

Method Detail

listOptions

setOptions

getOptions

desiredSizeTipText

numIterationsTipText

artificialSizeTipText

globalInfo

getTechnicalInformation

getArtificialSize

setArtificialSize

getDesiredSize

setDesiredSize

getCapabilities

buildClassifier

distributionForInstance

toString

getRevision

main