weka.classifiers.meta
Class Stacking

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.MultipleClassifiersCombiner
          weka.classifiers.ParallelMultipleClassifiersCombiner
              weka.classifiers.RandomizableParallelMultipleClassifiersCombiner
                  weka.classifiers.meta.Stacking

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

Direct Known Subclasses:

Grading, StackingC

public class Stacking
extends RandomizableParallelMultipleClassifiersCombiner
implements TechnicalInformationHandler

Combines several classifiers using the stacking method. Can do classification or regression.

For more information, see

David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.

BibTeX:

 @article{Wolpert1992,
    author = {David H. Wolpert},
    journal = {Neural Networks},
    pages = {241-259},
    publisher = {Pergamon Press},
    title = {Stacked generalization},
    volume = {5},
    year = {1992}
 }
 

Valid options are:

 -M <scheme specification>
  Full name of meta classifier, followed by options.
  (default: "weka.classifiers.rules.Zero")

 -X <number of folds>
  Sets the number of cross-validation folds.

 -S <num>
  Random number seed.
  (default 1)

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 5987 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Stacking()

Method Summary

void	buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
double[]	distributionForInstance(Instance instance) Returns class probabilities.
Capabilities	getCapabilities() Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
Classifier	getMetaClassifier() Gets the meta classifier.
int	getNumFolds() Gets the number of folds for the cross-validation.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision 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() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	metaClassifierTipText() Returns the tip text for this property
java.lang.String	numFoldsTipText() Returns the tip text for this property
void	setMetaClassifier(Classifier classifier) Adds meta classifier
void	setNumFolds(int numFolds) Sets the number of folds for the cross-validation.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Output a representation of this classifier

Methods inherited from class weka.classifiers.RandomizableParallelMultipleClassifiersCombiner

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.ParallelMultipleClassifiersCombiner

getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner

classifiersTipText, getClassifier, getClassifiers, setClassifiers

Methods inherited from class weka.classifiers.AbstractClassifier

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

Methods inherited from class java.lang.Object

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

Constructor Detail

Stacking

public Stacking()

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

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableParallelMultipleClassifiersCombiner

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -M <scheme specification>
  Full name of meta classifier, followed by options.
  (default: "weka.classifiers.rules.Zero")

 -X <number of folds>
  Sets the number of cross-validation folds.

 -S <num>
  Random number seed.
  (default 1)

 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableParallelMultipleClassifiersCombiner

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableParallelMultipleClassifiersCombiner

Returns:

an array of strings suitable for passing to setOptions

numFoldsTipText

public java.lang.String numFoldsTipText()

Returns the tip text for this property

Returns:

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

getNumFolds

public int getNumFolds()

Gets the number of folds for the cross-validation.

Returns:

the number of folds for the cross-validation

setNumFolds

public void setNumFolds(int numFolds)
                 throws java.lang.Exception

Sets the number of folds for the cross-validation.

Parameters:

numFolds - the number of folds for the cross-validation

Throws:

java.lang.Exception - if parameter illegal

metaClassifierTipText

public java.lang.String metaClassifierTipText()

Returns the tip text for this property

Returns:

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

setMetaClassifier

public void setMetaClassifier(Classifier classifier)

Adds meta classifier

Parameters:

classifier - the classifier with all options set.

getMetaClassifier

public Classifier getMetaClassifier()

Gets the meta classifier.

Returns:

the meta classifier

getCapabilities

public Capabilities getCapabilities()

Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class MultipleClassifiersCombiner

Returns:

the capabilities of the base classifiers

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class ParallelMultipleClassifiersCombiner

Parameters:

data - the training data to be used for generating the boosted classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Returns class probabilities.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the distribution

Throws:

java.lang.Exception - if instance could not be classified successfully

toString

public java.lang.String toString()

Output a representation of this classifier

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

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the following arguments: -t training file [-T test file] [-c class index]