Package weka.attributeSelection
Class ReliefFAttributeEval
- java.lang.Object
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- weka.attributeSelection.ASEvaluation
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- weka.attributeSelection.ReliefFAttributeEval
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- All Implemented Interfaces:
java.io.Serializable
,AttributeEvaluator
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class ReliefFAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler, TechnicalInformationHandler
ReliefFAttributeEval :
Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. Can operate on both discrete and continuous class data.
For more information see:
Kenji Kira, Larry A. Rendell: A Practical Approach to Feature Selection. In: Ninth International Workshop on Machine Learning, 249-256, 1992.
Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, 171-182, 1994.
Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, 296-304, 1997. BibTeX:@inproceedings{Kira1992, author = {Kenji Kira and Larry A. Rendell}, booktitle = {Ninth International Workshop on Machine Learning}, editor = {Derek H. Sleeman and Peter Edwards}, pages = {249-256}, publisher = {Morgan Kaufmann}, title = {A Practical Approach to Feature Selection}, year = {1992} } @inproceedings{Kononenko1994, author = {Igor Kononenko}, booktitle = {European Conference on Machine Learning}, editor = {Francesco Bergadano and Luc De Raedt}, pages = {171-182}, publisher = {Springer}, title = {Estimating Attributes: Analysis and Extensions of RELIEF}, year = {1994} } @inproceedings{Robnik-Sikonja1997, author = {Marko Robnik-Sikonja and Igor Kononenko}, booktitle = {Fourteenth International Conference on Machine Learning}, editor = {Douglas H. Fisher}, pages = {296-304}, publisher = {Morgan Kaufmann}, title = {An adaptation of Relief for attribute estimation in regression}, year = {1997} }
Valid options are:-M <num instances> Specify the number of instances to sample when estimating attributes. If not specified, then all instances will be used.
-D <seed> Seed for randomly sampling instances. (Default = 1)
-K <number of neighbours> Number of nearest neighbours (k) used to estimate attribute relevances (Default = 10).
-W Weight nearest neighbours by distance
-A <num> Specify sigma value (used in an exp function to control how quickly weights for more distant instances decrease. Use in conjunction with -W. Sensible value=1/5 to 1/10 of the number of nearest neighbours. (Default = 2)
- Version:
- $Revision: 11219 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description ReliefFAttributeEval()
Constructor
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildEvaluator(Instances data)
Initializes a ReliefF attribute evaluator.double
evaluateAttribute(int attribute)
Evaluates an individual attribute using ReliefF's instance based approach.Capabilities
getCapabilities()
Returns the capabilities of this evaluator.int
getNumNeighbours()
Get the number of nearest neighboursjava.lang.String[]
getOptions()
Gets the current settings of ReliefFAttributeEval.java.lang.String
getRevision()
Returns the revision string.int
getSampleSize()
Get the number of instances used for estimating attributesint
getSeed()
Get the seed used for randomly sampling instances.int
getSigma()
Get the value of sigma.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
getWeightByDistance()
Get whether nearest neighbours are being weighted by distancejava.lang.String
globalInfo()
Returns a string describing this attribute evaluatorjava.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
numNeighboursTipText()
Returns the tip text for this propertyint[]
postProcess(int[] attributeSet)
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.java.lang.String
sampleSizeTipText()
Returns the tip text for this propertyjava.lang.String
seedTipText()
Returns the tip text for this propertyvoid
setNumNeighbours(int n)
Set the number of nearest neighboursvoid
setOptions(java.lang.String[] options)
Parses a given list of options.void
setSampleSize(int s)
Set the number of instances to sample for attribute estimationvoid
setSeed(int s)
Set the random number seed for randomly sampling instances.void
setSigma(int s)
Sets the sigma value.void
setWeightByDistance(boolean b)
Set the nearest neighbour weighting methodjava.lang.String
sigmaTipText()
Returns the tip text for this propertyjava.lang.String
toString()
Return a description of the ReliefF attribute evaluator.java.lang.String
weightByDistanceTipText()
Returns the tip text for this property-
Methods inherited from class weka.attributeSelection.ASEvaluation
clean, forName, makeCopies
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing this attribute evaluator- Returns:
- a description of the evaluator 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
- 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:-M <num instances> Specify the number of instances to sample when estimating attributes. If not specified, then all instances will be used.
-D <seed> Seed for randomly sampling instances. (Default = 1)
-K <number of neighbours> Number of nearest neighbours (k) used to estimate attribute relevances (Default = 10).
-W Weight nearest neighbours by distance
-A <num> Specify sigma value (used in an exp function to control how quickly weights for more distant instances decrease. Use in conjunction with -W. Sensible value=1/5 to 1/10 of the number of nearest neighbours. (Default = 2)
- Specified by:
setOptions
in interfaceOptionHandler
- 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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sigmaTipText
public java.lang.String sigmaTipText()
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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setSigma
public void setSigma(int s) throws java.lang.Exception
Sets the sigma value.- Parameters:
s
- the value of sigma (> 0)- Throws:
java.lang.Exception
- if s is not positive
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getSigma
public int getSigma()
Get the value of sigma.- Returns:
- the sigma value.
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numNeighboursTipText
public java.lang.String numNeighboursTipText()
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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setNumNeighbours
public void setNumNeighbours(int n)
Set the number of nearest neighbours- Parameters:
n
- the number of nearest neighbours.
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getNumNeighbours
public int getNumNeighbours()
Get the number of nearest neighbours- Returns:
- the number of nearest neighbours
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seedTipText
public java.lang.String seedTipText()
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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setSeed
public void setSeed(int s)
Set the random number seed for randomly sampling instances.- Parameters:
s
- the random number seed.
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getSeed
public int getSeed()
Get the seed used for randomly sampling instances.- Returns:
- the random number seed.
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sampleSizeTipText
public java.lang.String sampleSizeTipText()
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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setSampleSize
public void setSampleSize(int s)
Set the number of instances to sample for attribute estimation- Parameters:
s
- the number of instances to sample.
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getSampleSize
public int getSampleSize()
Get the number of instances used for estimating attributes- Returns:
- the number of instances.
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weightByDistanceTipText
public java.lang.String weightByDistanceTipText()
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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setWeightByDistance
public void setWeightByDistance(boolean b)
Set the nearest neighbour weighting method- Parameters:
b
- true nearest neighbours are to be weighted by distance.
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getWeightByDistance
public boolean getWeightByDistance()
Get whether nearest neighbours are being weighted by distance- Returns:
- m_weightByDiffernce
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of ReliefFAttributeEval.- Specified by:
getOptions
in interfaceOptionHandler
- Returns:
- an array of strings suitable for passing to setOptions()
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toString
public java.lang.String toString()
Return a description of the ReliefF attribute evaluator.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a description of the evaluator as a String.
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getCapabilities
public Capabilities getCapabilities()
Returns the capabilities of this evaluator.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classASEvaluation
- Returns:
- the capabilities of this evaluator
- See Also:
Capabilities
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buildEvaluator
public void buildEvaluator(Instances data) throws java.lang.Exception
Initializes a ReliefF attribute evaluator.- Specified by:
buildEvaluator
in classASEvaluation
- Parameters:
data
- set of instances serving as training data- Throws:
java.lang.Exception
- if the evaluator has not been generated successfully
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evaluateAttribute
public double evaluateAttribute(int attribute) throws java.lang.Exception
Evaluates an individual attribute using ReliefF's instance based approach. The actual work is done by buildEvaluator which evaluates all features.- Specified by:
evaluateAttribute
in interfaceAttributeEvaluator
- Parameters:
attribute
- the index of the attribute to be evaluated- Returns:
- the "merit" of the attribute
- Throws:
java.lang.Exception
- if the attribute could not be evaluated
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classASEvaluation
- Returns:
- the revision
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postProcess
public int[] postProcess(int[] attributeSet)
Description copied from class:ASEvaluation
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.- Overrides:
postProcess
in classASEvaluation
- Parameters:
attributeSet
- the set of attributes found by the search- Returns:
- a possibly ranked list of postprocessed attributes
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main
public static void main(java.lang.String[] args)
Main method for testing this class.- Parameters:
args
- the options
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