ReliefFAttributeEval

java.lang.Object
- weka.attributeSelection.ASEvaluation
- - weka.attributeSelection.ReliefFAttributeEval

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

Constructor Summary

Constructors
Constructor and Description

ReliefFAttributeEval()
Constructor

Constructors
Constructor and Description
`ReliefFAttributeEval()` Constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and 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 neighbours
`java.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 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
`int[]`	`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 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
clean, forName, makeCopies

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 interface 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
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class ASEvaluation
    
    Returns:
    
    the revision
  - 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 class ASEvaluation
    
    Parameters:
    
    attributeSet - the set of attributes found by the search
    
    Returns:
    
    a possibly ranked list of postprocessed attributes
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for testing this class.
    
    Parameters:
    
    args - the options

Class ReliefFAttributeEval

Constructor Summary

Method Summary

Methods inherited from class weka.attributeSelection.ASEvaluation

Methods inherited from class java.lang.Object

Constructor Detail

ReliefFAttributeEval

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

sigmaTipText

setSigma

getSigma

numNeighboursTipText

setNumNeighbours

getNumNeighbours

seedTipText

setSeed

getSeed

sampleSizeTipText

setSampleSize

getSampleSize

weightByDistanceTipText

setWeightByDistance

getWeightByDistance

getOptions

toString

getCapabilities

buildEvaluator

evaluateAttribute

getRevision

postProcess

main