weka.classifiers.meta
Class END

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.IteratedSingleClassifierEnhancer
              extended by weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  extended by weka.classifiers.meta.END
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, 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:

 @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: 1.7 $
Author:
Eibe Frank, Lin Dong
See Also:
Serialized Form

Constructor Summary
END()
          Constructor.
 
Method Summary
 void buildClassifier(Instances data)
          Builds the committee of randomizable classifiers.
 double[] distributionForInstance(Instance instance)
          Calculates the class membership probabilities for the given test instance.
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 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
static void main(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.String 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
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

END

public END()
Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing classifier

Returns:
a description suitable for displaying in the explorer/experimenter gui

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

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 the committee of randomizable classifiers.

Overrides:
buildClassifier in class IteratedSingleClassifierEnhancer
Parameters:
data - the training data to be used for generating the bagged classifier.
Throws:
java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.

Overrides:
distributionForInstance in class Classifier
Parameters:
instance - the instance to be classified
Returns:
preedicted class probability distribution
Throws:
java.lang.Exception - if distribution can't be computed successfully

toString

public java.lang.String toString()
Returns description of the committee.

Overrides:
toString in class java.lang.Object
Returns:
description of the committee as a string

main

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

Parameters:
argv - the options