weka.classifiers.meta

Class Dagging

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.RandomizableSingleClassifierEnhancer

weka.classifiers.meta.Dagging

All Implemented Interfaces:

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

public class Dagging
extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier.
Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data.

For more information, see:
Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

BibTeX:

 @inproceedings{Ting1997,
    address = {San Francisco, CA},
    author = {Ting, K. M. and Witten, I. H.},
    booktitle = {Fourteenth international Conference on Machine Learning},
    editor = {D. H. Fisher},
    pages = {367-375},
    publisher = {Morgan Kaufmann Publishers},
    title = {Stacking Bagged and Dagged Models},
    year = {1997}
 }
 

Valid options are:

 -F <folds>
  The number of folds for splitting the training set into
  smaller chunks for the base classifier.
  (default 10)

 -verbose
  Whether to print some more information during building the
  classifier.
  (default is off)

 -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.SMO)

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

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

 -no-checks
  Turns off all checks - use with caution!
  Turning them off assumes that data is purely numeric, doesn't
  contain any missing values, and has a nominal class. Turning them
  off also means that no header information will be stored if the
  machine is linear. Finally, it also assumes that no instance has
  a weight equal to 0.
  (default: checks on)

 -C <double>
  The complexity constant C. (default 1)

 -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

 -L <double>
  The tolerance parameter. (default 1.0e-3)

 -P <double>
  The epsilon for round-off error. (default 1.0e-12)

 -M
  Fit logistic models to SVM outputs. 

 -V <double>
  The number of folds for the internal
  cross-validation. (default -1, use training data)

 -W <double>
  The random number seed. (default 1)

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)

 
 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 

 -D
  Enables debugging output (if available) to be printed.
  (default: off)

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)

 -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)

 -E <num>
  The Exponent to use.
  (default: 1.0)

 -L
  Use lower-order terms.
  (default: no)

Options after -- are passed to the designated classifier.

Version:

$Revision: 5306 $

Author:

Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz), FracPete (fracpete at waikato dot ac dot nz)

See Also:

Vote, Serialized Form

Constructor Summary

Constructors

Constructor and Description
Dagging() Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Bagging method.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
int	getNumFolds() Gets the number of folds to use for splitting the training set.
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.
boolean	getVerbose() Gets the verbose state
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[] args) Main method for testing this class.
java.lang.String	numFoldsTipText() Returns the tip text for this property
void	setNumFolds(int value) Sets the number of folds to use for splitting the training set.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setVerbose(boolean value) Set the verbose state.
java.lang.String	toString() Returns description of the classifier.
java.lang.String	verboseTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getCapabilities, 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

Dagging

public Dagging()

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

listOptions

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

Parses a given list of options.

Valid options are:

 -F <folds>
  The number of folds for splitting the training set into
  smaller chunks for the base classifier.
  (default 10)

 -verbose
  Whether to print some more information during building the
  classifier.
  (default is off)

 -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.SMO)

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

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

 -no-checks
  Turns off all checks - use with caution!
  Turning them off assumes that data is purely numeric, doesn't
  contain any missing values, and has a nominal class. Turning them
  off also means that no header information will be stored if the
  machine is linear. Finally, it also assumes that no instance has
  a weight equal to 0.
  (default: checks on)

 -C <double>
  The complexity constant C. (default 1)

 -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

 -L <double>
  The tolerance parameter. (default 1.0e-3)

 -P <double>
  The epsilon for round-off error. (default 1.0e-12)

 -M
  Fit logistic models to SVM outputs. 

 -V <double>
  The number of folds for the internal
  cross-validation. (default -1, use training data)

 -W <double>
  The random number seed. (default 1)

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)

 
 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 

 -D
  Enables debugging output (if available) to be printed.
  (default: off)

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)

 -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)

 -E <num>
  The Exponent to use.
  (default: 1.0)

 -L
  Use lower-order terms.
  (default: no)

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableSingleClassifierEnhancer

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 RandomizableSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

getNumFolds

public int getNumFolds()

Gets the number of folds to use for splitting the training set.

Returns:

the number of folds

setNumFolds

public void setNumFolds(int value)

Sets the number of folds to use for splitting the training set.

Parameters:

value - the new number of folds

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

setVerbose

public void setVerbose(boolean value)

Set the verbose state.

Parameters:

value - the verbose state

getVerbose

public boolean getVerbose()

Gets the verbose state

Returns:

the verbose state

verboseTipText

public java.lang.String verboseTipText()

Returns the tip text for this property

Returns:

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

buildClassifier

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

Bagging method.

Specified by:

buildClassifier in class Classifier

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 classifier.

Overrides:

toString in class java.lang.Object

Returns:

description of the classifier as a string

getRevision

public java.lang.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(java.lang.String[] args)

Main method for testing this class.

Parameters:

args - the options