weka.classifiers.meta.nestedDichotomies
Class DataNearBalancedND

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.RandomizableSingleClassifierEnhancer
              extended by weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
All Implemented Interfaces:
java.io.Serializable, java.lang.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:

 @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:
Serialized Form

Constructor Summary
DataNearBalancedND()
          Constructor.
 
Method Summary
 void buildClassifier(Instances data)
          Builds tree recursively.
 double[] distributionForInstance(Instance inst)
          Predicts the class distribution for a given instance
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 java.lang.String getRevision()
          Returns the revision string.
 java.lang.String getString(int[] indices)
          Returns the list of indices as a 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()
           
static void main(java.lang.String[] argv)
          Main method for testing this class.
 void setHashtable(java.util.Hashtable table)
          Set hashtable from END.
 java.lang.String 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
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

DataNearBalancedND

public DataNearBalancedND()
Constructor.

Method Detail

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

setHashtable

public void setHashtable(java.util.Hashtable table)
Set hashtable from END.

Parameters:
table - the hashtable to use

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
Builds tree recursively.

Specified by:
buildClassifier in class Classifier
Parameters:
data - contains the (multi-class) instances
Throws:
java.lang.Exception - if the building fails

distributionForInstance

public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
Predicts the class distribution for a given instance

Overrides:
distributionForInstance in class Classifier
Parameters:
inst - the (multi-class) instance to be classified
Returns:
the class distribution
Throws:
java.lang.Exception - if computing fails

getString

public java.lang.String getString(int[] indices)
Returns the list of indices as a string.

Parameters:
indices - the indices to return as string
Returns:
the indices as string

globalInfo

public java.lang.String globalInfo()
Returns:
a description of the classifier suitable for displaying in the explorer/experimenter gui

toString

public java.lang.String toString()
Outputs the classifier as a string.

Overrides:
toString in class java.lang.Object
Returns:
a string representation of the classifier

getRevision

public java.lang.String getRevision()
Returns the revision string.

Specified by:
getRevision in interface RevisionHandler
Returns:
the revision

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options