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java.lang.Object | +----weka.attributeSelection.ASEvaluation | +----weka.attributeSelection.AttributeEvaluator | +----weka.attributeSelection.ReliefFAttributeEval
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
-D
-K
-W
-A
-M
-D
-K
-W
-A
Specify the number of instances to sample when estimating attributes.
If not specified then all instances will be used.
Seed for randomly sampling instances.
Number of nearest neighbours to use for estimating attributes.
(Default is 10).
Weight nearest neighbours by distance.
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.
ReliefFAttributeEval()
buildEvaluator(Instances)
evaluateAttribute(int)
getNumNeighbours()
getOptions()
getSampleSize()
getSeed()
getSigma()
getWeightByDistance()
globalInfo()
listOptions()
main(String[])
numNeighboursTipText()
sampleSizeTipText()
seedTipText()
setNumNeighbours(int)
setOptions(String[])
setSampleSize(int)
setSeed(int)
setSigma(int)
setWeightByDistance(boolean)
sigmaTipText()
toString()
weightByDistanceTipText()
ReliefFAttributeEval
public ReliefFAttributeEval()
globalInfo
public String globalInfo()
listOptions
public Enumeration listOptions()
setOptions
public void setOptions(String options[]) throws Exception
Specify the number of instances to sample when estimating attributes.
If not specified then all instances will be used.
Seed for randomly sampling instances.
Number of nearest neighbours to use for estimating attributes.
(Default is 10).
Weight nearest neighbours by distance.
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.
sigmaTipText
public String sigmaTipText()
setSigma
public void setSigma(int s) throws Exception
getSigma
public int getSigma()
numNeighboursTipText
public String numNeighboursTipText()
setNumNeighbours
public void setNumNeighbours(int n)
getNumNeighbours
public int getNumNeighbours()
seedTipText
public String seedTipText()
setSeed
public void setSeed(int s)
getSeed
public int getSeed()
sampleSizeTipText
public String sampleSizeTipText()
setSampleSize
public void setSampleSize(int s)
getSampleSize
public int getSampleSize()
weightByDistanceTipText
public String weightByDistanceTipText()
setWeightByDistance
public void setWeightByDistance(boolean b)
getWeightByDistance
public boolean getWeightByDistance()
getOptions
public String[] getOptions()
toString
public String toString()
buildEvaluator
public void buildEvaluator(Instances data) throws Exception
evaluateAttribute
public double evaluateAttribute(int attribute) throws Exception
main
public static void main(String args[])
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