Class DataNearBalancedND
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.RandomizableSingleClassifierEnhancer
weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
public class DataNearBalancedND
extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.
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)
-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).
- Author:
- Lin Dong, Eibe Frank
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances data) Builds tree recursively.double[]
Predicts the class distribution for a given instanceReturns default capabilities of the classifier.Returns the revision string.getString
(int[] indices) Returns the list of indices as a 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.static void
Main method for testing this class.void
setHashtable
(Hashtable table) Set hashtable from END.toString()
Outputs the classifier as a string.Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
getOptions, getSeed, listOptions, seedTipText, setOptions, setSeed
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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DataNearBalancedND
public DataNearBalancedND()Constructor.
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Method Details
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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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setHashtable
Set hashtable from END.- Parameters:
table
- the hashtable to use
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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:
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buildClassifier
Builds tree recursively.- Specified by:
buildClassifier
in classClassifier
- Parameters:
data
- contains the (multi-class) instances- Throws:
Exception
- if the building fails
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distributionForInstance
Predicts the class distribution for a given instance- Overrides:
distributionForInstance
in classClassifier
- Parameters:
inst
- the (multi-class) instance to be classified- Returns:
- the class distribution
- Throws:
Exception
- if computing fails
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getString
Returns the list of indices as a string.- Parameters:
indices
- the indices to return as string- Returns:
- the indices as string
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globalInfo
- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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toString
Outputs the classifier as a string. -
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
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