Package weka.classifiers.meta
Class RotationForest
- java.lang.Object
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- 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
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Constructor Summary
Constructors Constructor Description RotationForest()
Constructor.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method 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 sizejava.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 removedjava.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 classifierjava.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 propertyjava.lang.String
minGroupTipText()
Returns the tip text for this propertyjava.lang.String
numberOfGroupsTipText()
Returns the tip text for this propertyjava.lang.String
projectionFilterTipText()
Returns the tip text for this propertyjava.lang.String
removedPercentageTipText()
Returns the tip text for this propertyvoid
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 sizevoid
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 removedjava.lang.String
toString()
Returns description of the Rotation Forest classifier.-
Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
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Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
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Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getCapabilities, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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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 interfaceOptionHandler
- Overrides:
setOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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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
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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
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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
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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
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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
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getMinGroup
public int getMinGroup()
Gets the minimum size of a group.- Returns:
- the minimum value.
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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
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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
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getMaxGroup
public int getMaxGroup()
Gets the maximum size of a group.- Returns:
- the maximum value.
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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
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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
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getRemovedPercentage
public int getRemovedPercentage()
Gets the percentage of instances to be removed- Returns:
- the percentage.
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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
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setProjectionFilter
public void setProjectionFilter(Filter projectionFilter)
Sets the filter used to project the data.- Parameters:
projectionFilter
- the filter.
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getProjectionFilter
public Filter getProjectionFilter()
Gets the filter used to project the data.- Returns:
- the filter.
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toString
public java.lang.String toString()
Returns description of the Rotation Forest classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- description of the Rotation Forest classifier as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
builds the classifier.- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the training data to be used for generating the classifier.- Throws:
java.lang.Exception
- if the classifier could not be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- preedicted class probability distribution
- Throws:
java.lang.Exception
- if distribution can't be computed successfully
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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