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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
weka.classifiers.meta.Decorate
public class Decorate
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..
@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.
Constructor Summary | |
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Decorate()
Constructor. |
Method Summary | |
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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 |
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getSeed, seedTipText, setSeed |
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer |
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getNumIterations, setNumIterations |
Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
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classifierTipText, getClassifier, setClassifier |
Methods inherited from class weka.classifiers.Classifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public Decorate()
Method Detail |
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public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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.
setOptions
in interface OptionHandler
setOptions
in class RandomizableIteratedSingleClassifierEnhancer
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableIteratedSingleClassifierEnhancer
public java.lang.String desiredSizeTipText()
public java.lang.String numIterationsTipText()
numIterationsTipText
in class IteratedSingleClassifierEnhancer
public java.lang.String artificialSizeTipText()
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public double getArtificialSize()
public void setArtificialSize(double newArtSize)
newArtSize
- factor that determines number of artificial examples to generatepublic int getDesiredSize()
public void setDesiredSize(int newDesiredSize)
newDesiredSize
- the desired size of the committeepublic Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the classifier
java.lang.Exception
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classified
java.lang.Exception
- if distribution can't be computed successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- the options
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