Package weka.classifiers.meta
Class END
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
public class END
extends RandomizableIteratedSingleClassifierEnhancer
implements TechnicalInformationHandler
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005.
Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004. BibTeX:
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005.
Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004. BibTeX:
@inproceedings{Dong2005, author = {Lin Dong and Eibe Frank and Stefan Kramer}, booktitle = {PKDD}, pages = {84-95}, publisher = {Springer}, title = {Ensembles of Balanced Nested Dichotomies for Multi-class Problems}, year = {2005} } @inproceedings{Frank2004, author = {Eibe Frank and Stefan Kramer}, booktitle = {Twenty-first International Conference on Machine Learning}, publisher = {ACM}, title = {Ensembles of nested dichotomies for multi-class problems}, year = {2004} }Valid options are:
-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.meta.nestedDichotomies.ND)
Options specific to classifier weka.classifiers.meta.nestedDichotomies.ND:
-S <num> Random number seed. (default 1)
-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:
- Eibe Frank, Lin Dong
- See Also:
-
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances data) Builds the committee of randomizable classifiers.double[]
distributionForInstance
(Instance instance) Calculates the class membership probabilities for the given test instance.Returns default capabilities of the classifier.Returns the revision string.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.Returns a string describing classifierstatic void
Main method for testing this class.toString()
Returns description of the committee.Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getOptions, getSeed, listOptions, seedTipText, setOptions, 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
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Constructor Details
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END
public END()Constructor.
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
-
buildClassifier
Builds the committee of randomizable classifiers.- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the training data to be used for generating the bagged classifier.- Throws:
Exception
- if the classifier could not be built successfully
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distributionForInstance
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- preedicted class probability distribution
- Throws:
Exception
- if distribution can't be computed successfully
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toString
Returns description of the committee. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- the options
-