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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

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: 1.6 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

ReliefFAttributeEval()

Constructor

buildEvaluator(Instances)

Initializes a ReliefF attribute evaluator.

evaluateAttribute(int)

Evaluates an individual attribute using ReliefF's instance based approach.

getNumNeighbours()

Get the number of nearest neighbours

getOptions()

Gets the current settings of ReliefFAttributeEval.

getSampleSize()

Get the number of instances used for estimating attributes

getSeed()

Get the seed used for randomly sampling instances.

getSigma()

Get the value of sigma.

getWeightByDistance()

Get whether nearest neighbours are being weighted by distance

listOptions()

Returns an enumeration describing the available options

main(String[])

Main method for testing this class.

setNumNeighbours(int)

Set the number of nearest neighbours

setOptions(String[])

Parses a given list of options.

setSampleSize(int)

Set the number of instances to sample for attribute estimation

setSeed(int)

Set the random number seed for randomly sampling instances.

setSigma(int)

Sets the sigma value.

setWeightByDistance(boolean)

Set the nearest neighbour weighting method

toString()

Return a description of the ReliefF attribute evaluator.

 public ReliefFAttributeEval()

Constructor

 public Enumeration listOptions()

Returns an enumeration describing the available options

Returns:

an enumeration of all the available options

 public void setOptions(String options[]) throws 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: Exception

if an option is not supported

 public void setSigma(int s) throws Exception

Sets the sigma value.

Parameters:

s - the value of sigma (> 0)

Throws: Exception

if s is not positive

 public int getSigma()

Get the value of sigma.

Returns:

the sigma value.

 public void setNumNeighbours(int n)

Set the number of nearest neighbours

Parameters:

n - the number of nearest neighbours.

 public int getNumNeighbours()

Get the number of nearest neighbours

Returns:

the number of nearest neighbours

 public void setSeed(int s)

Set the random number seed for randomly sampling instances.

Parameters:

s - the random number seed.

 public int getSeed()

Get the seed used for randomly sampling instances.

Returns:

the random number seed.

 public void setSampleSize(int s)

Set the number of instances to sample for attribute estimation

Parameters:

s - the number of instances to sample.

 public int getSampleSize()

Get the number of instances used for estimating attributes

Returns:

the number of instances.

 public void setWeightByDistance(boolean b)

Set the nearest neighbour weighting method

Parameters:

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

 public boolean getWeightByDistance()

Get whether nearest neighbours are being weighted by distance

Returns:

m_weightByDiffernce

 public String[] getOptions()

Gets the current settings of ReliefFAttributeEval.

Returns:

an array of strings suitable for passing to setOptions()

 public String toString()

Return a description of the ReliefF attribute evaluator.

Returns:

a description of the evaluator as a String.

Overrides:

toString in class Object

 public void buildEvaluator(Instances data) throws Exception

Initializes a ReliefF attribute evaluator.

Parameters:

data - set of instances serving as training data

Throws: Exception

if the evaluator has not been generated successfully

Overrides:

buildEvaluator in class ASEvaluation

 public double evaluateAttribute(int attribute) throws 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: Exception

if the attribute could not be evaluated

Overrides:

evaluateAttribute in class AttributeEvaluator

 public static void main(String args[])

Main method for testing this class.

Parameters:

args - the options

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