weka.classifiers.meta.nestedDichotomies

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:

 @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

Constructors

Constructor and Description
DataNearBalancedND() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
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.
String	getRevision() Returns the revision string.
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.
String	globalInfo()
static void	main(String[] argv) Main method for testing this class.
void	setHashtable(Hashtable table) Set hashtable from END.
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(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 Exception

Builds tree recursively.

Specified by:

buildClassifier in class Classifier

Parameters:

data - contains the (multi-class) instances

Throws:

Exception - if the building fails

distributionForInstance

public double[] distributionForInstance(Instance inst)
                                 throws 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:

Exception - if computing fails

getString

public 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 String globalInfo()

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

toString

public String toString()

Outputs the classifier as a string.

Overrides:

toString in class Object

Returns:

a string representation of the classifier

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options