public class RotationForest extends RandomizableIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, TechnicalInformationHandler
@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).
Constructor and Description |
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RotationForest()
Constructor.
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Modifier and Type | Method and Description |
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void |
buildClassifier(Instances data)
builds the classifier.
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double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test
instance.
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int |
getMaxGroup()
Gets the maximum size of a group.
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int |
getMinGroup()
Gets the minimum size of a group.
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boolean |
getNumberOfGroups()
Get whether minGroup and maxGroup refer to the number of groups or their
size
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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Filter |
getProjectionFilter()
Gets the filter used to project the data.
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int |
getRemovedPercentage()
Gets the percentage of instances to be removed
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java.lang.String |
getRevision()
Returns the revision string.
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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.
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java.lang.String |
globalInfo()
Returns a string describing classifier
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java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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java.lang.String |
maxGroupTipText()
Returns the tip text for this property
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java.lang.String |
minGroupTipText()
Returns the tip text for this property
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java.lang.String |
numberOfGroupsTipText()
Returns the tip text for this property
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java.lang.String |
projectionFilterTipText()
Returns the tip text for this property
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java.lang.String |
removedPercentageTipText()
Returns the tip text for this property
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void |
setMaxGroup(int maxGroup)
Sets the maximum size of a group.
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void |
setMinGroup(int minGroup)
Sets the minimum size of a group.
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void |
setNumberOfGroups(boolean numberOfGroups)
Set whether minGroup and maxGroup refer to the number of groups or their
size
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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void |
setProjectionFilter(Filter projectionFilter)
Sets the filter used to project the data.
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void |
setRemovedPercentage(int removedPercentage)
Sets the percentage of instance to be removed
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java.lang.String |
toString()
Returns description of the Rotation Forest classifier.
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getSeed, seedTipText, setSeed
getNumIterations, numIterationsTipText, setNumIterations
classifierTipText, getCapabilities, getClassifier, setClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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).
setOptions
in interface OptionHandler
setOptions
in class RandomizableIteratedSingleClassifierEnhancer
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableIteratedSingleClassifierEnhancer
public java.lang.String numberOfGroupsTipText()
public void setNumberOfGroups(boolean numberOfGroups)
numberOfGroups
- whether minGroup and maxGroup refer to the number
of groups or their sizepublic boolean getNumberOfGroups()
public java.lang.String minGroupTipText()
public void setMinGroup(int minGroup) throws java.lang.IllegalArgumentException
minGroup
- the minimum value.
of attributes.java.lang.IllegalArgumentException
public int getMinGroup()
public java.lang.String maxGroupTipText()
public void setMaxGroup(int maxGroup) throws java.lang.IllegalArgumentException
maxGroup
- the maximum value.
of attributes.java.lang.IllegalArgumentException
public int getMaxGroup()
public java.lang.String removedPercentageTipText()
public void setRemovedPercentage(int removedPercentage) throws java.lang.IllegalArgumentException
removedPercentage
- the percentage.java.lang.IllegalArgumentException
public int getRemovedPercentage()
public java.lang.String projectionFilterTipText()
public void setProjectionFilter(Filter projectionFilter)
projectionFilter
- the filter.public Filter getProjectionFilter()
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the
classifier.java.lang.Exception
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if distribution can't be computed successfullypublic static void main(java.lang.String[] argv)
argv
- the options