Package weka.classifiers.meta
Class Decorate
- java.lang.Object
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- 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
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Constructor Summary
Constructors Constructor Description Decorate()
Constructor.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description java.lang.String
artificialSizeTipText()
Returns the tip text for this propertyvoid
buildClassifier(Instances data)
Build Decorate classifierjava.lang.String
desiredSizeTipText()
Returns the tip text for this propertydouble[]
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 classifierjava.util.Enumeration
listOptions()
Returns an enumeration describing the available optionsstatic void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
numIterationsTipText()
Returns the tip text for this propertyvoid
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
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Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, setNumIterations
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Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options
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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 interfaceOptionHandler
- Overrides:
setOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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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
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numIterationsTipText
public java.lang.String numIterationsTipText()
Returns the tip text for this property- Overrides:
numIterationsTipText
in classIteratedSingleClassifierEnhancer
- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getArtificialSize
public double getArtificialSize()
Factor that determines number of artificial examples to generate.- Returns:
- factor that determines number of artificial examples to generate
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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
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getDesiredSize
public int getDesiredSize()
Gets the desired size of the committee.- Returns:
- the desired size of the committee
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setDesiredSize
public void setDesiredSize(int newDesiredSize)
Sets the desired size of the committee.- Parameters:
newDesiredSize
- the desired size of the committee
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Build Decorate classifier- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the training data to be used for generating the classifier- Throws:
java.lang.Exception
- if the classifier could not be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- predicted class probability distribution
- Throws:
java.lang.Exception
- if distribution can't be computed successfully
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toString
public java.lang.String toString()
Returns description of the Decorate classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- description of the Decorate classifier as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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