weka.classifiers.meta

Class RotationForest

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

weka.classifiers.meta.RotationForest

All Implemented Interfaces:

Serializable, Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class RotationForest
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, TechnicalInformationHandler

Class for construction a Rotation Forest. Can do classification and regression depending on the base learner.

For more information, see

Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630. URL http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211.

BibTeX:

 @article{Rodriguez2006,
    author = {Juan J. Rodriguez and Ludmila I. Kuncheva and Carlos J. Alonso},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    number = {10},
    pages = {1619-1630},
    title = {Rotation Forest: A new classifier ensemble method},
    volume = {28},
    year = {2006},
    ISSN = {0162-8828},
    URL = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211}
 }
 

Valid options are:

 -N
  Whether minGroup (-G) and maxGroup (-H) refer to
  the number of groups or their size.
  (default: false)

 -G <num>
  Minimum size of a group of attributes:
   if numberOfGroups is true, the minimum number
   of groups.
   (default: 3)

 -H <num>
  Maximum size of a group of attributes:
   if numberOfGroups is true, the maximum number
   of groups.
   (default: 3)

 -P <num>
  Percentage of instances to be removed.
   (default: 50)

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  eg: "weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0"

 -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.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).

Version:

$Revision: 7012 $

Author:

Juan Jose Rodriguez ([email protected])

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RotationForest() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances data) builds the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
int	getMaxGroup() Gets the maximum size of a group.
int	getMinGroup() Gets the minimum size of a group.
boolean	getNumberOfGroups() Get whether minGroup and maxGroup refer to the number of groups or their size
String[]	getOptions() Gets the current settings of the Classifier.
Filter	getProjectionFilter() Gets the filter used to project the data.
int	getRemovedPercentage() Gets the percentage of instances to be removed
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.
String	globalInfo() Returns a string describing classifier
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
String	maxGroupTipText() Returns the tip text for this property
String	minGroupTipText() Returns the tip text for this property
String	numberOfGroupsTipText() Returns the tip text for this property
String	projectionFilterTipText() Returns the tip text for this property
String	removedPercentageTipText() Returns the tip text for this property
void	setMaxGroup(int maxGroup) Sets the maximum size of a group.
void	setMinGroup(int minGroup) Sets the minimum size of a group.
void	setNumberOfGroups(boolean numberOfGroups) Set whether minGroup and maxGroup refer to the number of groups or their size
void	setOptions(String[] options) Parses a given list of options.
void	setProjectionFilter(Filter projectionFilter) Sets the filter used to project the data.
void	setRemovedPercentage(int removedPercentage) Sets the percentage of instance to be removed
String	toString() Returns description of the Rotation Forest classifier.

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

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

RotationForest

public RotationForest()

Constructor.

Method Detail

globalInfo

public 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 Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableIteratedSingleClassifierEnhancer

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:

 -N
  Whether minGroup (-G) and maxGroup (-H) refer to
  the number of groups or their size.
  (default: false)

 -G <num>
  Minimum size of a group of attributes:
   if numberOfGroups is true, the minimum number
   of groups.
   (default: 3)

 -H <num>
  Maximum size of a group of attributes:
   if numberOfGroups is true, the maximum number
   of groups.
   (default: 3)

 -P <num>
  Percentage of instances to be removed.
   (default: 50)

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  eg: "weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0"

 -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.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).

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableIteratedSingleClassifierEnhancer

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 RandomizableIteratedSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

numberOfGroupsTipText

public String numberOfGroupsTipText()

Returns the tip text for this property

Returns:

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

setNumberOfGroups

public void setNumberOfGroups(boolean numberOfGroups)

Set whether minGroup and maxGroup refer to the number of groups or their size

Parameters:

numberOfGroups - whether minGroup and maxGroup refer to the number of groups or their size

getNumberOfGroups

public boolean getNumberOfGroups()

Get whether minGroup and maxGroup refer to the number of groups or their size

Returns:

whether minGroup and maxGroup refer to the number of groups or their size

minGroupTipText

public String minGroupTipText()

Returns the tip text for this property

Returns:

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

setMinGroup

public void setMinGroup(int minGroup)
                 throws IllegalArgumentException

Sets the minimum size of a group.

Parameters:

minGroup - the minimum value. of attributes.

Throws:

IllegalArgumentException

getMinGroup

public int getMinGroup()

Gets the minimum size of a group.

Returns:

the minimum value.

maxGroupTipText

public String maxGroupTipText()

Returns the tip text for this property

Returns:

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

setMaxGroup

public void setMaxGroup(int maxGroup)
                 throws IllegalArgumentException

Sets the maximum size of a group.

Parameters:

maxGroup - the maximum value. of attributes.

Throws:

IllegalArgumentException

getMaxGroup

public int getMaxGroup()

Gets the maximum size of a group.

Returns:

the maximum value.

removedPercentageTipText

public String removedPercentageTipText()

Returns the tip text for this property

Returns:

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

setRemovedPercentage

public void setRemovedPercentage(int removedPercentage)
                          throws IllegalArgumentException

Sets the percentage of instance to be removed

Parameters:

removedPercentage - the percentage.

Throws:

IllegalArgumentException

getRemovedPercentage

public int getRemovedPercentage()

Gets the percentage of instances to be removed

Returns:

the percentage.

projectionFilterTipText

public String projectionFilterTipText()

Returns the tip text for this property

Returns:

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

setProjectionFilter

public void setProjectionFilter(Filter projectionFilter)

Sets the filter used to project the data.

Parameters:

projectionFilter - the filter.

getProjectionFilter

public Filter getProjectionFilter()

Gets the filter used to project the data.

Returns:

the filter.

toString

public String toString()

Returns description of the Rotation Forest classifier.

Overrides:

toString in class Object

Returns:

description of the Rotation Forest classifier as a string

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

builds the classifier.

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

Parameters:

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

Throws:

Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws 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:

Exception - if distribution can't be computed successfully

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options