weka.attributeSelection
Class ReliefFAttributeEval

java.lang.Object
  extended by weka.attributeSelection.ASEvaluation
      extended by weka.attributeSelection.AttributeEvaluator
          extended by weka.attributeSelection.ReliefFAttributeEval
All Implemented Interfaces:
java.io.Serializable, CapabilitiesHandler, OptionHandler, TechnicalInformationHandler

public class ReliefFAttributeEval
extends AttributeEvaluator
implements 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: 1.21 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
ReliefFAttributeEval()
          Constructor
 
Method Summary
 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 neighbours
 java.lang.String[] getOptions()
          Gets the current settings of ReliefFAttributeEval.
 int getSampleSize()
          Get the number of instances used for estimating attributes
 int 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 distance
 java.lang.String globalInfo()
          Returns a string describing this attribute evaluator
 java.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 property
 java.lang.String sampleSizeTipText()
          Returns the tip text for this property
 java.lang.String seedTipText()
          Returns the tip text for this property
 void setNumNeighbours(int n)
          Set the number of nearest neighbours
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setSampleSize(int s)
          Set the number of instances to sample for attribute estimation
 void 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 method
 java.lang.String sigmaTipText()
          Returns the tip text for this property
 java.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
forName, makeCopies, postProcess
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

ReliefFAttributeEval

public ReliefFAttributeEval()
Constructor

Method Detail

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

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
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:

 -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 interface OptionHandler
Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported

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

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

getSigma

public int getSigma()
Get the value of sigma.

Returns:
the sigma value.

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

setNumNeighbours

public void setNumNeighbours(int n)
Set the number of nearest neighbours

Parameters:
n - the number of nearest neighbours.

getNumNeighbours

public int getNumNeighbours()
Get the number of nearest neighbours

Returns:
the number of nearest neighbours

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

setSeed

public void setSeed(int s)
Set the random number seed for randomly sampling instances.

Parameters:
s - the random number seed.

getSeed

public int getSeed()
Get the seed used for randomly sampling instances.

Returns:
the random number seed.

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

setSampleSize

public void setSampleSize(int s)
Set the number of instances to sample for attribute estimation

Parameters:
s - the number of instances to sample.

getSampleSize

public int getSampleSize()
Get the number of instances used for estimating attributes

Returns:
the number of instances.

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

setWeightByDistance

public void setWeightByDistance(boolean b)
Set the nearest neighbour weighting method

Parameters:
b - true nearest neighbours are to be weighted by distance.

getWeightByDistance

public boolean getWeightByDistance()
Get whether nearest neighbours are being weighted by distance

Returns:
m_weightByDiffernce

getOptions

public java.lang.String[] getOptions()
Gets the current settings of ReliefFAttributeEval.

Specified by:
getOptions in interface OptionHandler
Returns:
an array of strings suitable for passing to setOptions()

toString

public java.lang.String toString()
Return a description of the ReliefF attribute evaluator.

Overrides:
toString in class java.lang.Object
Returns:
a description of the evaluator as a String.

getCapabilities

public Capabilities getCapabilities()
Returns the capabilities of this evaluator.

Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class ASEvaluation
Returns:
the capabilities of this evaluator
See Also:
Capabilities

buildEvaluator

public void buildEvaluator(Instances data)
                    throws java.lang.Exception
Initializes a ReliefF attribute evaluator.

Specified by:
buildEvaluator in class ASEvaluation
Parameters:
data - set of instances serving as training data
Throws:
java.lang.Exception - if the evaluator has not been generated successfully

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 class AttributeEvaluator
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

main

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

Parameters:
args - the options