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Class weka.attributeSelection.ReliefFAttributeEval

java.lang.Object
    |
    +----weka.attributeSelection.ASEvaluation
            |
            +----weka.attributeSelection.AttributeEvaluator
                    |
                    +----weka.attributeSelection.ReliefFAttributeEval

public class ReliefFAttributeEval
extends AttributeEvaluator
implements OptionHandler
Class for Evaluating attributes individually using ReliefF.

For more information see:

Kira, K. and Rendell, L. A. (1992). A practical approach to feature selection. In D. Sleeman and P. Edwards, editors, Proceedings of the International Conference on Machine Learning, pages 249-256. Morgan Kaufmann.

Kononenko, I. (1994). Estimating attributes: analysis and extensions of Relief. In De Raedt, L. and Bergadano, F., editors, Machine Learning: ECML-94, pages 171-182. Springer Verlag.

Marko Robnik Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation on regression. In D.Fisher (ed.): Machine Learning, Proceedings of 14th International Conference on Machine Learning ICML'97, Nashville, TN, 1997.

Valid options are: -M
Specify the number of instances to sample when estimating attributes.
If not specified then all instances will be used.

-D
Seed for randomly sampling instances.

-K
Number of nearest neighbours to use for estimating attributes.
(Default is 10).

-W
Weight nearest neighbours by distance.

-A
Specify sigma value (used in an exp function to control how quickly
weights decrease for more distant instances). Use in conjunction with
-W. Sensible values = 1/5 to 1/10 the number of nearest neighbours.

Version:
$Revision: 1.12 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)

Constructor Index

 o ReliefFAttributeEval()
Constructor

Method Index

 o buildEvaluator(Instances)
Initializes a ReliefF attribute evaluator.
 o evaluateAttribute(int)
Evaluates an individual attribute using ReliefF's instance based approach.
 o getNumNeighbours()
Get the number of nearest neighbours
 o getOptions()
Gets the current settings of ReliefFAttributeEval.
 o getSampleSize()
Get the number of instances used for estimating attributes
 o getSeed()
Get the seed used for randomly sampling instances.
 o getSigma()
Get the value of sigma.
 o getWeightByDistance()
Get whether nearest neighbours are being weighted by distance
 o globalInfo()
Returns a string describing this attribute evaluator
 o listOptions()
Returns an enumeration describing the available options
 o main(String[])
Main method for testing this class.
 o numNeighboursTipText()
Returns the tip text for this property
 o sampleSizeTipText()
Returns the tip text for this property
 o seedTipText()
Returns the tip text for this property
 o setNumNeighbours(int)
Set the number of nearest neighbours
 o setOptions(String[])
Parses a given list of options.
 o setSampleSize(int)
Set the number of instances to sample for attribute estimation
 o setSeed(int)
Set the random number seed for randomly sampling instances.
 o setSigma(int)
Sets the sigma value.
 o setWeightByDistance(boolean)
Set the nearest neighbour weighting method
 o sigmaTipText()
Returns the tip text for this property
 o toString()
Return a description of the ReliefF attribute evaluator.
 o weightByDistanceTipText()
Returns the tip text for this property

Constructor Detail

 o ReliefFAttributeEval
public ReliefFAttributeEval()
          Constructor

Method Detail

 o 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
 o listOptions
public java.util.Enumeration listOptions()
          Returns an enumeration describing the available options
Returns:
an enumeration of all the available options
 o setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
          Parses a given list of options. Valid options are:

-M
Specify the number of instances to sample when estimating attributes.
If not specified then all instances will be used.

-D
Seed for randomly sampling instances.

-K
Number of nearest neighbours to use for estimating attributes.
(Default is 10).

-W
Weight nearest neighbours by distance.

-A
Specify sigma value (used in an exp function to control how quickly
weights decrease for more distant instances). Use in conjunction with
-W. Sensible values = 1/5 to 1/10 the number of nearest neighbours.

Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported
 o 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
 o 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
 o getSigma
public int getSigma()
          Get the value of sigma.
Returns:
the sigma value.
 o 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
 o setNumNeighbours
public void setNumNeighbours(int n)
          Set the number of nearest neighbours
Parameters:
n - the number of nearest neighbours.
 o getNumNeighbours
public int getNumNeighbours()
          Get the number of nearest neighbours
Returns:
the number of nearest neighbours
 o 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
 o setSeed
public void setSeed(int s)
          Set the random number seed for randomly sampling instances.
Parameters:
s - the random number seed.
 o getSeed
public int getSeed()
          Get the seed used for randomly sampling instances.
Returns:
the random number seed.
 o 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
 o setSampleSize
public void setSampleSize(int s)
          Set the number of instances to sample for attribute estimation
Parameters:
s - the number of instances to sample.
 o getSampleSize
public int getSampleSize()
          Get the number of instances used for estimating attributes
Returns:
the number of instances.
 o 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
 o setWeightByDistance
public void setWeightByDistance(boolean b)
          Set the nearest neighbour weighting method
Parameters:
b - true nearest neighbours are to be weighted by distance.
 o getWeightByDistance
public boolean getWeightByDistance()
          Get whether nearest neighbours are being weighted by distance
Returns:
m_weightByDiffernce
 o getOptions
public java.lang.String[] getOptions()
          Gets the current settings of ReliefFAttributeEval.
Returns:
an array of strings suitable for passing to setOptions()
 o toString
public java.lang.String toString()
          Return a description of the ReliefF attribute evaluator.
Returns:
a description of the evaluator as a String.
Overrides:
toString in class java.lang.Object
 o buildEvaluator
public void buildEvaluator(Instances data) throws java.lang.Exception
          Initializes a ReliefF attribute evaluator.
Parameters:
data - set of instances serving as training data
Throws:
java.lang.Exception - if the evaluator has not been generated successfully
Overrides:
buildEvaluator in class ASEvaluation
 o 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.
Parameters:
attribute - the index of the attribute to be evaluated
Throws:
java.lang.Exception - if the attribute could not be evaluated
Overrides:
evaluateAttribute in class AttributeEvaluator
 o main
public static void main(java.lang.String args[])
          Main method for testing this class.
Parameters:
args - the options

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