weka.classifiers.meta

Class MultiClassClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.RandomizableSingleClassifierEnhancer

weka.classifiers.meta.MultiClassClassifier

All Implemented Interfaces:

Serializable, Cloneable, Classifier, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler

Direct Known Subclasses:

MultiClassClassifierUpdateable

public class MultiClassClassifier
extends RandomizableSingleClassifierEnhancer
implements OptionHandler

A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for increased accuracy.

Valid options are:

 -M <num>
  Sets the method to use. Valid values are 0 (1-against-all),
  1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
 

 -R <num>
  Sets the multiplier when using random codes. (default 2.0)

 -P
  Use pairwise coupling (only has an effect for 1-against1)

 -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.functions.Logistic)

 
 Options specific to classifier weka.classifiers.functions.Logistic:
 

 -D
  Turn on debugging output.

 -R <ridge>
  Set the ridge in the log-likelihood.

 -M <number>
  Set the maximum number of iterations (default -1, until convergence).

Version:

$Revision: 8034 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	METHOD_1_AGAINST_1 1-against-1
static int	METHOD_1_AGAINST_ALL 1-against-all
static int	METHOD_ERROR_EXHAUSTIVE exhaustive correction code
static int	METHOD_ERROR_RANDOM random correction code
static Tag[]	TAGS_METHOD The error correction modes

Constructor Summary

Constructors

Constructor and Description
MultiClassClassifier() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances insts) Builds the classifiers.
double[]	distributionForInstance(Instance inst) Returns the distribution for an instance.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
SelectedTag	getMethod() Gets the method used.
String[]	getOptions() Gets the current settings of the Classifier.
double	getRandomWidthFactor() Gets the multiplier when generating random codes.
String	getRevision() Returns the revision string.
boolean	getUsePairwiseCoupling() Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
String	globalInfo()
double[]	individualPredictions(Instance inst) Returns the individual predictions of the base classifiers for an instance.
Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(String[] argv) Main method for testing this class.
String	methodTipText()
static double[]	pairwiseCoupling(double[][] n, double[][] r) Implements pairwise coupling.
String	randomWidthFactorTipText()
void	setMethod(SelectedTag newMethod) Sets the method used.
void	setOptions(String[] options) Parses a given list of options.
void	setRandomWidthFactor(double newRandomWidthFactor) Sets the multiplier when generating random codes.
void	setUsePairwiseCoupling(boolean p) Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
String	toString() Prints the classifiers.
String	usePairwiseCouplingTipText()

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

METHOD_1_AGAINST_ALL

public static final int METHOD_1_AGAINST_ALL

1-against-all

See Also:

Constant Field Values

METHOD_ERROR_RANDOM

public static final int METHOD_ERROR_RANDOM

random correction code

See Also:

Constant Field Values

METHOD_ERROR_EXHAUSTIVE

public static final int METHOD_ERROR_EXHAUSTIVE

exhaustive correction code

See Also:

Constant Field Values

METHOD_1_AGAINST_1

public static final int METHOD_1_AGAINST_1

1-against-1

See Also:

Constant Field Values

TAGS_METHOD

public static final Tag[] TAGS_METHOD

The error correction modes

Constructor Detail

MultiClassClassifier

public MultiClassClassifier()

Constructor.

Method Detail

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface 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 insts)
                     throws Exception

Builds the classifiers.

Specified by:

buildClassifier in interface Classifier

Parameters:

insts - the training data.

Throws:

Exception - if a classifier can't be built

individualPredictions

public double[] individualPredictions(Instance inst)
                               throws Exception

Returns the individual predictions of the base classifiers for an instance. Used by StackedMultiClassClassifier. Returns the probability for the second "class" predicted by each base classifier.

Parameters:

inst - the instance to get the prediction for

Returns:

the individual predictions

Throws:

Exception - if the predictions can't be computed successfully

distributionForInstance

public double[] distributionForInstance(Instance inst)
                                 throws Exception

Returns the distribution for an instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

inst - the instance to get the distribution for

Returns:

the distribution

Throws:

Exception - if the distribution can't be computed successfully

toString

public String toString()

Prints the classifiers.

Overrides:

toString in class Object

Returns:

a string representation of the classifier

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableSingleClassifierEnhancer

Returns:

an enumeration of all the available options

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -M <num>
  Sets the method to use. Valid values are 0 (1-against-all),
  1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
 

 -R <num>
  Sets the multiplier when using random codes. (default 2.0)

 -P
  Use pairwise coupling (only has an effect for 1-against1)

 -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.functions.Logistic)

 
 Options specific to classifier weka.classifiers.functions.Logistic:
 

 -D
  Turn on debugging output.

 -R <ridge>
  Set the ridge in the log-likelihood.

 -M <number>
  Set the maximum number of iterations (default -1, until convergence).

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableSingleClassifierEnhancer

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

globalInfo

public String globalInfo()

Returns:

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

randomWidthFactorTipText

public String randomWidthFactorTipText()

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getRandomWidthFactor

public double getRandomWidthFactor()

Gets the multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes.

Returns:

the width multiplier

setRandomWidthFactor

public void setRandomWidthFactor(double newRandomWidthFactor)

Sets the multiplier when generating random codes. Will generate numClasses * m_RandomWidthFactor codes.

Parameters:

newRandomWidthFactor - the new width multiplier

methodTipText

public String methodTipText()

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getMethod

public SelectedTag getMethod()

Gets the method used. Will be one of METHOD_1_AGAINST_ALL, METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1.

Returns:

the current method.

setMethod

public void setMethod(SelectedTag newMethod)

Sets the method used. Will be one of METHOD_1_AGAINST_ALL, METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1.

Parameters:

newMethod - the new method.

setUsePairwiseCoupling

public void setUsePairwiseCoupling(boolean p)

Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.

Parameters:

p - true if pairwise coupling is to be used

getUsePairwiseCoupling

public boolean getUsePairwiseCoupling()

Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.

Returns:

true if pairwise coupling is to be used

usePairwiseCouplingTipText

public String usePairwiseCouplingTipText()

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

pairwiseCoupling

public static double[] pairwiseCoupling(double[][] n,
                        double[][] r)

Implements pairwise coupling.

Parameters:

n - the sum of weights used to train each model

r - the probability estimate from each model

Returns:

the coupled estimates

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options