weka.classifiers.meta
Class RotationForest

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.IteratedSingleClassifierEnhancer
              extended by weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                  extended by weka.classifiers.meta.RotationForest
All Implemented Interfaces:
java.io.Serializable, java.lang.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 (jjrodriguez@ubu.es)
See Also:
Serialized Form

Constructor Summary
RotationForest()
          Constructor.
 
Method Summary
 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
 java.lang.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
 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 maxGroupTipText()
          Returns the tip text for this property
 java.lang.String minGroupTipText()
          Returns the tip text for this property
 java.lang.String numberOfGroupsTipText()
          Returns the tip text for this property
 java.lang.String projectionFilterTipText()
          Returns the tip text for this property
 java.lang.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(java.lang.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
 java.lang.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 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 RandomizableIteratedSingleClassifierEnhancer
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:

 -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:
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 RandomizableIteratedSingleClassifierEnhancer
Returns:
an array of strings suitable for passing to setOptions

numberOfGroupsTipText

public java.lang.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 java.lang.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 java.lang.IllegalArgumentException
Sets the minimum size of a group.

Parameters:
minGroup - the minimum value. of attributes.
Throws:
java.lang.IllegalArgumentException

getMinGroup

public int getMinGroup()
Gets the minimum size of a group.

Returns:
the minimum value.

maxGroupTipText

public java.lang.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 java.lang.IllegalArgumentException
Sets the maximum size of a group.

Parameters:
maxGroup - the maximum value. of attributes.
Throws:
java.lang.IllegalArgumentException

getMaxGroup

public int getMaxGroup()
Gets the maximum size of a group.

Returns:
the maximum value.

removedPercentageTipText

public java.lang.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 java.lang.IllegalArgumentException
Sets the percentage of instance to be removed

Parameters:
removedPercentage - the percentage.
Throws:
java.lang.IllegalArgumentException

getRemovedPercentage

public int getRemovedPercentage()
Gets the percentage of instances to be removed

Returns:
the percentage.

projectionFilterTipText

public java.lang.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 java.lang.String toString()
Returns description of the Rotation Forest classifier.

Overrides:
toString in class java.lang.Object
Returns:
description of the Rotation Forest 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

buildClassifier

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

Overrides:
buildClassifier in class IteratedSingleClassifierEnhancer
Parameters:
data - the training data to be used for generating the 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

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options